Today, computer aided system is widely used in various fields. Among them, the brain tumor detection is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of brain tumors for cancer diagnosis, from large amount of Magnetic Resonance Imaging MRI images generated in clinical routine, is a difficult and time consuming task or even generates errors. So, the automatic brain tumor segmentation is needed to segment tumor. The purpose of the thesis is to detect the brain tumor quickly and accurately from the MRI brain image. In the system, the average filter is used to remove noise and make smooth an input MRI image and threshold segmentation is applied to segment tumor region from MRI brain images. Region properties method is used to detect the tumor region exactly. And then, the equation of the tumor region in the system is effectively applied in any shape of the tumor region. Moe Moe Aye | Kyaw Kyaw Lin "Brain Tumor Detection System for MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27864.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/27864/brain-tumor-detection-system-for-mri-image/moe-moe-aye
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
One of the most dangerous disease occurring these days i.e. brain tumor can be detected by MRI images. Biomedical imaging and medical image processing that plays a vital role for MRI images has now become the most challenging field in engineering and technology. A detailed information about the anatomy can be showed through MRI images, that helps in monitoring the disease and is beneficial for the diagnosis as it consists of a high tissue contrast and have fewer artifacts. For tracking the disease and to proceed its treatment, MRI images plays a key role. It is having several advantages over other imaging techniques and is an important step for post-processing of medical images. However, having a large amount of data for manual analysis can sometimes proved to be an obstacle in the way of its effective use. In this paper, the introduction of image processing and the details of image segmentation techniques such as image preprocessing, feature extraction, image enhancement and classification of tumor processes, and how image segmentation can be applied to all Other available imaging modalities that are different from one another. This paper provides the survey on various methods used for image segmentation that have been applied for MRI images, that detects the tumor by segmenting the brain images into constituent parts. Also the advantages and disadvantages of Image segmentation is discussed using the various approaches of image segmentation of MRI brain images.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
One of the most dangerous disease occurring these days i.e. brain tumor can be detected by MRI images. Biomedical imaging and medical image processing that plays a vital role for MRI images has now become the most challenging field in engineering and technology. A detailed information about the anatomy can be showed through MRI images, that helps in monitoring the disease and is beneficial for the diagnosis as it consists of a high tissue contrast and have fewer artifacts. For tracking the disease and to proceed its treatment, MRI images plays a key role. It is having several advantages over other imaging techniques and is an important step for post-processing of medical images. However, having a large amount of data for manual analysis can sometimes proved to be an obstacle in the way of its effective use. In this paper, the introduction of image processing and the details of image segmentation techniques such as image preprocessing, feature extraction, image enhancement and classification of tumor processes, and how image segmentation can be applied to all Other available imaging modalities that are different from one another. This paper provides the survey on various methods used for image segmentation that have been applied for MRI images, that detects the tumor by segmenting the brain images into constituent parts. Also the advantages and disadvantages of Image segmentation is discussed using the various approaches of image segmentation of MRI brain images.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vi...ijscmcj
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
BATA-UNET: DEEP LEARNING MODEL FOR LIVER SEGMENTATIONsipij
In computer vision, image segmentation is defined as process of a partition of an image in a number of regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning revolution traditional handcrafted features were used for liver segmentation but with deep learning the features are obtained automatically. There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using 3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in we added a batch normalization layer an after each convolution layer in both construction path and expanding path. The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for
3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we compare it with them.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vi...ijscmcj
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Brain tumors can affect white matter fibers by either infiltrating or displacing the tissue. When the myelin sheath is damaged or disappears, the conduction of impulses along nerve fibers slows down or fails completely. Diffusion Tensor Imaging (DTI) is a relatively new imaging technique that can be used to evaluate white matter in the brain. DTI has diagnostic implications by being able to pinpoint areas where normal water flow is disrupted, providing valuable information about the location of specific lesions. Edema, infiltration and destruction of white matter reduces the anisotropic nature of the white matter. The paper aims to segment tumor from the healthy brain tissues in Diffusion Tensor brain tumor images using Fuzzy C-Means clustering.
BATA-UNET: DEEP LEARNING MODEL FOR LIVER SEGMENTATIONsipij
In computer vision, image segmentation is defined as process of a partition of an image in a number of regions with homogeneous features. The region of our interest here is the liver. Prior to the deep learning revolution traditional handcrafted features were used for liver segmentation but with deep learning the features are obtained automatically. There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a Classical Based method for Segmentation. In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal to 0.91% when implement our BATA Convnet using MICCA dataset and Dice is equal to 0.84% when implement it using 3D-IRCAD dataset. Here in this paper we propose BATA-Unet model for liver segmentation, it's based on Unet architecture as backbone but differ in we added a batch normalization layer an after each convolution layer in both construction path and expanding path. The proposed method was able to achieve highest dice similarity coefficient than the previous work where for MICCA dataset Dice =0.97% and for
3D-IRCAD dataset =0.96%. Also our proposed model outperformed other state-of-the-art model when we compare it with them.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
MRI Image Segmentation by Using DWT for Detection of Brain Tumorijtsrd
Brain tumor segmentation is one of the critical tasks in the medical image processing. Some early diagnosis of brain tumor helps in improving the treatment and also increases the survival rate of the patients. The manual segmentation for cancer diagnosis of brain tumor and generation of MRI images in clinical routine is difficult and time consuming. The aim of this research paper is to review of MRI based brain tumor segmentation methods for the treatment of cancer like diseases. The magnetic resonance imaging used for detection of tumor and diagnosis of tissue abnormalities. The computerized medical image segmentation helps the doctors in treatment in a simple way with fast decision making. The brain tumor segmentation assessed by computer based surgery, tumor growth, developing tumor growth models and treatment responses. This research focuses on the causes of brain tumor, brain tumor segmentation and its classification, MRI scanning process and different segmentation methodologies. Ishu Rana | Gargi Kalia | Preeti Sondhi ""MRI Image Segmentation by Using DWT for Detection of Brain Tumor"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25116.pdf
Paper URL: https://www.ijtsrd.com/computer-science/bioinformatics/25116/mri-image-segmentation-by-using-dwt-for-detection-of-brain-tumor/ishu-rana
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
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2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD27864 | Volume – 3 | Issue – 5 | July - August 2019 Page 2071
Devkota, et al. [2] have proposed a computeraideddetection
approach to diagnose brain tumor in its early stage using
Mathematical Morphological Reconstruction (MMR). Image
is pre-processed to remove noise and artifacts and then
segmented to find regions of interest withprobabletumor.A
large number of textural and statistical features are
extracted from the segmented image to classify whether the
brain tumor in the image is benign or malignant. Their
experimental results showthat thesegmented images havea
high accuracy while substantially reducing the computation
time. Their study shows that the proposed solution can be
used to diagnose brain tumor in patients with a high success
rate.
A., Meena, and K., Raja proposed an approach of Spatial
Fuzzy C-Means (PETSFCM) clustering algorithmonPositron
Emission Tomography (PET) scan image datasets.
The algorithm is joining the spatial neighborhood
information with classical Fuzzy C-Means (FCM) and
updating the objective function of each cluster. Spatial
relationship of neighboring pixel is an aid of image
segmentation.Theseneighboringpixels arehighlyrenovated
the same feature data. In spatial domain, the member-ships
of the neighbor centered are specified to obtain the cluster
distribution statistics. They calculated the weighting
function based on these statistics and applied into the
member-ship function. Their algorithm is tested on data
collection of patients with Alzheimer’s disease. They did not
calculate objective based quality assessment that could
analyze images and did not report their quality without
human involvement.
METHODOLOGY
This session expresses the background theory used in the
brain tumor detection system. Average filter, image
segmentation, region properties and calculation of tumor
area in MRI brain images are explained in the following
sections.
A. Average Filter
The default filter type is the average or mean filter in the
general class. The average filter is low pass filter. Theoutput
cell value calculated by filter is the simple average
(arithmetic mean) of the cells in the filter window. The
averaging performed by the average filter removes some of
the higher frequency features, while allowing the low-
frequency features to pass through the filter unchanged
(thus the term “low pass” filter). This has the effect of
smoothing the raster image, emphasizing its larger-scale
brightness trends. Whether the smoothing produced by the
low pass filter is beneficial or not depends on the
characteristics of the input image. Average filter is used to
smooth the image and remove noisessuchas Gaussiannoise.
It reduces the amount of intensity variation between one
pixel and the next.
Average filter is an estimation of average from neighbor
pixels within the kernel. The filtering equation can be
expressed as (1).
g(x,y) = 1/M∑(x,y)∈S f(x,y) (1)
where S is the neighborhood of pixel (x,y), M is the number
of pixels in neighborhood S, and g(x,y) is the filtered image.
Sub-images are summed up and then multiplied by 1/M [4].
B. Image Segmentation
Thresholding is one of the commonly used methods for
image segmentation [5]. Image threshold is a simple,
effective and way of partitioning an image into a foreground
and background. This image analysis technique is a type of
image segmentation that isolates objects by converting
grayscale images into binary images.Regions ofanimage are
separated out corresponding to objects which want to
analyze. Each pixel is labeled as belonging to one of
(typically) two classes. At present, threshold-based methods
are classified into global, local andadaptivethresholdings. In
this study, global threshold based segmentation method is
used to segment brain tumor in MRI brain image.
Global (single) thresholding method is used when the
intensity distribution between the objects offoreground and
background are very distinct. When the differences between
foreground and background objects are very distinct, a
single value of threshold can simply be used to differentiate
both objects apart [6]. Global methods finda singlethreshold
value T for the whole document. Then each pixel is assigned
to page foreground or background based on its gray value
comparing with thethresholdvalue.Globalmethods arevery
fast and they give good results for typical scanned
documents. For many years, the binarization of a grayscale
document was based on the global thresholding statistical
algorithms [7]. Somemostcommonusedglobalthresholding
methods are Otsu method, entropy based thresholding, etc.
Otsu’s algorithm is a popular global thresholding technique.
Moreover, there are many popular thresholding techniques
such as Kittler and Illingworth, Kapur, Tasi, Huang, Yen and
et al [8]. The condition for selecting the T is given as (2).
g(x, y) =
1 if f(x, y) > T
0 otherwise
(2)
where g(x, y) is the thresholded image, f (x, y) is the input
image, and T is the threshold value. Equation (2) has no
indication on selecting the threshold value T. The threshold
T separates the object from the dark background. Any point
(x,y) for which f(x, y) >T is called an object point. Pixels are
labeled 1 corresponds to object whereas pixels labeled 0
corresponds to the background [8]. Global thresholding is
popular due to simplicity and easy implementation [5][9].
C. Region Properties
Image regions are also called objects, connected
components, or blobs, can be contiguous or discontiguous.It
returns measurements for the set of properties specified by
properties for 8-connected component(object) inthebinary
image, black and white. A region in an image can have
properties, such as Area, BoundingBox, ConvexArea,
Centroid, Eccentricity, ConvexHull, EquivDiameter,
EulerNumber, Extent, Extrema, FilledArea, Image,
FilledImage, MajorAxisLength, MinorAxisLength,
Orientation, PixelList, PixelIdxList, Solidity, SubarrayIdx,
Perimeter, ConvexImage. Measurement values are returned
as an array of structure or a table. Region properties canbea
comma-separated list of strings, a cell array containing
strings, the single string 'all', or the string 'basic'. If
properties are the string 'all', then all the preceding
measurements are computed. If properties are not specified
or are the string 'basic', then these measurements are
computed: 'Area', 'Centroid', and 'BoundingBox' [10].
3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD27864 | Volume – 3 | Issue – 5 | July - August 2019 Page 2072
D. Area Calculation of Tumor Region
The tumor area is calculated using the binarization method.
That is the image having only two values either black or
white (0 or 1) [11]. 256x256 jpeg image is the maximum
image size. The binary image can be represented as a
summation of total number of white and black pixels shown
in (3). The number of white pixels is calculated by (4).
Image I= ∑ ∑ [f(0)+f(1)]255
H=0
255
W=0 (3)
where f(0) is black pixel (digit 0) and f(1)is whitepixel (digit
1).
P = ∑ ∑ [f(0)] (4)
, where P is number of white pixel and 1 Pixel is equal to
0.264 mm.
The area calculation formula is shown in (5).
Size_of_tumor_is, S=[(√A)*0.264]mm2 (5)
DESIGN OF THE SYSTEM
The typical brain tumor detection generally consists of five
basic steps: image pre-processing, tumor segmentation,
tumor detection, calculation of the brain tumor area and
determination of the brain tumor location. The flow chart of
the system is shown in Fig. 1.
Figure1. Flow Chart of the Proposed System
At first, MRI image of the brain is accepted as input image of
the system. In pre-processing step, the input MRI brain
image is converted into grayscale image. When MRI/CT
images are viewed, they look like black and white but they
contain some primary colors (RGB). Therefore, grayscale
conversion is needed in this step. This result is put to the
filtering step using average filter for smoothing the input
image. Several different filters can be used for filtering. But,
MRI image does not contain a lot of noise. So, the average
filter is used in this step. After using the average filter, the
smooth image is obtained. This smooth image is used to
operate the next step of the system quickly.
After removing the noise from the input brain image, this
image is loaded into the segmentation step. In this step,
global thresholding is used and the threshold point 0.6 is
selected as standard threshold point for this segmentation
step. This threshold point is very effective for all MRI brain
images. If a suitable threshold point is not chosen, the
detection of the brain tumor region can be difficult to find
the accurate tumor region. Therefore, an appropriate
threshold point is chosen for the system. The segmented
images can have pixels above the standard threshold point.
The image from the segmentation process is labeled to use
region properties. Many region properties of the image may
be used to detect the tumor region, but only two region
properties are used in the system such as ‘Area’ and
‘Solidity’. Solidity gives the proportion of the pixels in the
convex hull that are also in the region. So, solidity is what
fraction of the actual area region is.Forany convexobject it’s
1. An asterisk might have a solidity of around 0.5, while a
thin “L” shape (or “T” or “E”, etc.) would have a very low
solidity. Moreover, solidity also helps to find the normal
brain which is no tumor in the tumor detection stage. And
then area of the region properties is usedtofindthemember
of the result from the defined solidityvalue.Afterthesesteps
are finished, the brain tumor region is obtained quickly and
accurately.
In this step, the output from the detection process is used to
calculate the area of the tumor region in the brain slice. The
size of images, 256 × 256, are used as the maximum image
size for the system so that the dimensions of the MRI brain
images are equal size. Therefore,thecalculationofthetumor
region is more efficient to analyze for medical fields. This
region properties method generates automatically tumor
area in pixels. So the white pixels of tumor region from the
detected brain image are without needed to count.
Finally, the location of the tumor region can be determined
to know position whether tumor is at left hemisphere, right
hemisphere or near the center of the brain. The column
pixels or x-dimension of the first input image from the
system is divided by 2. The minimum value and the
maximum value are calculated to compare with the center
value of image which is divided result of input image. If the
tumor is located in the left part of the brain, the tumor is
actually located in the right hemisphere of the brain or
inversely. If the minimum value is greater than the center
value, the tumor is located in the left hemisphere of the
brain. If the maximum value is less than the center value,the
tumor is located in the right hemisphere of the brain.
Otherwise, the tumor is located near the center of the brain.
4. International Journal of Trend in Scientific Research and Development (IJTSRD)
@ IJTSRD | Unique Paper ID – IJTSRD27864
IMPLEMENTATION RESULTS
There are forty-seven MRI brain images collected to detect
brain tumor. Thirty-six brain tumor images and eleven
normal brain images are tested. The step by step results of
the brain tumor detection system are shown in Fig. 2.
Figure2. Results of the Brain Tumor Detection
If the tumor is detected, the area and location of the brain
tumor region are calculated and displayed. If not, the result
will be displayed as Fig. 3.
Figure3. Result for Normal Brain
CONCLUSION
The brain tumor detection system for MRI images has been
presented in this paper. The calculationoftumorarea playsa
vital role in assisting the treatment planning. The stage of
tumor is based on the area of tumor. In theproposedsystem,
till the small size of brain tumor, 3 mm2, can
we found that if the area of the brain tumor is greater than 6
mm2, it will be critical position. The segmentation and
detection methods are low cost as it can be implemented in
general computer. Although the computer aided techniques
are complex to be implemented but notas tedious,laborious,
and time consuming as manual methods. Therefore, the
system will help doctors to take or analyze in which stage of
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com
27864 | Volume – 3 | Issue – 5 | July - August 2019
seven MRI brain images collected to detect
six brain tumor images and eleven
normal brain images are tested. The step by step results of
the brain tumor detection system are shown in Fig. 2.
rain Tumor Detection
If the tumor is detected, the area and location of the brain
tumor region are calculated and displayed. If not, the result
3. Result for Normal Brain
for MRI images has been
presented in this paper. The calculationoftumorarea playsa
vital role in assisting the treatment planning. The stage of
tumor is based on the area of tumor. In theproposedsystem,
till the small size of brain tumor, 3 mm2, can bedetected and
we found that if the area of the brain tumor is greater than 6
mm2, it will be critical position. The segmentation and
detection methods are low cost as it can be implemented in
general computer. Although the computer aided techniques
omplex to be implemented but notas tedious,laborious,
and time consuming as manual methods. Therefore, the
system will help doctors to take or analyze in which stage of
cancer the patient suffers and to take necessary and
appropriate treatment steps.
REFERENCES
[1] K. Surabhi, “An Efficient BrainTumorDetectionSystem
Based on Segmentation Technique for MRI Brain
Images,” International Journal ofAdvancedResearchin
Computer Science, Volume 8, No.7,pp.1131
– August 2017.
[2] Devkota, et al., “Image Segmentation for Early Stage
Brain Tumor Detection using Mathematical
Morphological Reconstruction”, Procedia Computer
Science, vol. 125. Pp. 115
[3] Meena, and K. Raja, “Spatial Fuzzy C
Segmentation of Neurodegenerative Disorde
International Journal of Computer Science and
Engineering (IJCSE), 2013.
[4] Gonzalez, et al.:“Digital Image Processing”, Second
Edition, Prentice Hall 2002
[5] S. A. Salem, N. V. Kalyankar, and S. D. Khamitkar,
“Image Segmentation by using Thershod Techniques
Journal of Computing, vol2, pp 83
[6] N. Senthilkumaran, and S. Vaithegi, “Image
Segmentation by Using Thresholding Techniques for
Medical Images”, Computer Science & Engineering: An
International Journal (CSEIJ), Vol.6, No.1, February
2016
[7] G. S. Evelin, Y. V. S. Lakshmi, and G. J. Wiselin, “MRI
Brain Image Segmentation based on Thresholding”,
International Journal of Advanced ComputerResearch,
Volume-3 Number-1, Issue
[8] G. K. Priyanka, and N. H. Sushilkumar, “A Review of
Image Thresholding Techniques”,InternationalJournal
of Advanced Research in Computer Science and
Software Engineering, Volume 5, Issue 6, June 2015
[9] S. Abutaleb, “Automatic Thresholding of Gray
Pictures using Two Dimensional Entropy”, Computer
Vision, Grapics, and Image processing, vol.47, pp. 22
32, 1989
[10] Anonymous, “Image Region Properties”. The
MathWorks, Inc., 2018
[11] J. Selvakumar, A. Lakshmi, and T. Arivoli, “BrainTumor
Segmentation and Its Area Calculation in Brain MR
Images using K-Mean Clustering an
Algorithm”, IEEE-International Conference On
Advances In Engineering, Science And Management
(ICAESM -2012) March 30, 31, 2012
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August 2019 Page 2073
cancer the patient suffers and to take necessary and
appropriate treatment steps.
K. Surabhi, “An Efficient BrainTumorDetectionSystem
Based on Segmentation Technique for MRI Brain
Images,” International Journal ofAdvancedResearchin
Computer Science, Volume 8, No.7,pp.1131-1136,July
mage Segmentation for Early Stage
Brain Tumor Detection using Mathematical
Morphological Reconstruction”, Procedia Computer
Science, vol. 125. Pp. 115-123, 2018.
Meena, and K. Raja, “Spatial Fuzzy C-means PET Image
Segmentation of Neurodegenerative Disorder”,
International Journal of Computer Science and
Engineering (IJCSE), 2013.
Gonzalez, et al.:“Digital Image Processing”, Second
Edition, Prentice Hall 2002
S. A. Salem, N. V. Kalyankar, and S. D. Khamitkar,
“Image Segmentation by using Thershod Techniques”,
Journal of Computing, vol2, pp 83-86, 2010
N. Senthilkumaran, and S. Vaithegi, “Image
Segmentation by Using Thresholding Techniques for
Medical Images”, Computer Science & Engineering: An
International Journal (CSEIJ), Vol.6, No.1, February
velin, Y. V. S. Lakshmi, and G. J. Wiselin, “MRI
Brain Image Segmentation based on Thresholding”,
International Journal of Advanced ComputerResearch,
1, Issue-8, March-2013
G. K. Priyanka, and N. H. Sushilkumar, “A Review of
Thresholding Techniques”,InternationalJournal
of Advanced Research in Computer Science and
Software Engineering, Volume 5, Issue 6, June 2015
S. Abutaleb, “Automatic Thresholding of Gray -Level
Pictures using Two Dimensional Entropy”, Computer
apics, and Image processing, vol.47, pp. 22-
Anonymous, “Image Region Properties”. The
J. Selvakumar, A. Lakshmi, and T. Arivoli, “BrainTumor
Segmentation and Its Area Calculation in Brain MR
Mean Clustering and Fuzzy C-Mean
International Conference On
Advances In Engineering, Science And Management
2012) March 30, 31, 2012
![International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 5, August 2019
@ IJTSRD | Unique Paper ID – IJTSRD27864
Brain Tumor Detection System
Moe Moe Aye
1Department of Information Technology,
1, 2Technological University
How to cite this paper: Moe Moe Aye |
Kyaw Kyaw Lin "Brain Tumor Detection
System for MRI
Image" Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-3 |
Issue-5, August 2019, pp.2070-2073,
https://doi.org/10.31142/ijtsrd27864
Copyright © 2019 by author(s) and
International Journal ofTrend inScientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
Commons Attribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by
/4.0)
The brain tumor detection is very hard in beginning st
because it cannot find the accurate measurement of tumor.
Thus, the brain tumor detection still remains challenging
problem due to complex structureof brainthanotherbreast,
heart, lung, kidney, bone cancer detections in medical fields.
Tumor segmentation from MRI data is an important process
but it is time-consuming and difficult task often performed
manually by medical experts. Radiologists andothermedical
experts spend a substantial amount of time segmenting
medical images when a large number of MRI brain images
are analyzed. Moreover,accuratelylabeling braintumormay
lead to missing diagnosis. And it is a considerable variation
that is observed between doctors. To avoid human based
diagnostic error,computeraideddiagnosis systemis needed.
Throughout the few years, different segmentation methods
have been used for tumor detection but it is time consuming
process and also gives inaccurate result. And most of them
fail because of unknown noises, poor image contrast, in
homogeneity and weak boundaries that are usualinmedical
images. In this paper, the Brain Tumor Detection System is
designed to detect the brain tumor from MRI brain images
accurately in order to enhance the performanceoftheimage
segmentation especially in the field of brain
segmentation.
Implementation and identification of brain tumor are done
by segmentation and detecting thebrainimagefromtheMRI
scans. In the system, pre-processing of the image is the
primary step which removes noises and smoothes the input
MRI brain image. And, segmentation is carried out using
thresholding technique. Then, the brain tumor region is
detected from the segmented image using region properties.
IJTSRD27864
International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 5, August 2019 Available Online: www.ijtsrd.com e-
27864 | Volume – 3 | Issue – 5 | July - August 2019
Brain Tumor Detection System for MRI Image
Moe Moe Aye1, Kyaw Kyaw Lin2
f Information Technology, 2Department of Mechatronic Engineering
echnological University, Mandalay, Myanmar
http://creativecommons.org/licenses/by
ABSTRACT
Today, computer aided system is widely used in various fields. Among them,
the brain tumor detection is an important task in medical image processing.
Early diagnosis of brain tumors plays an important role in improving
treatment possibilities and increases the survival rate of the patients. Manual
segmentation of brain tumors for cancer diagnosis, from large amount of
Magnetic Resonance Imaging (MRI) images generated in c
difficult and time consuming task or even generates errors. So, the automatic
brain tumor segmentation is needed to segment tumor. The purpose of the
thesis is to detect the brain tumor quickly and accurately from the MRI brain
image. In the system, the average filter is used to remove noise and make
smooth an input MRI image and threshold segmentationis appliedto segment
tumor region from MRI brain images. Region properties method is used to
detect the tumor region exactly. And then, the equation of the tumor region in
the system is effectively applied in any shape of the tumor region.
KEYWORDS: area of tumor; brain tumor detection; location in brain;MRIimage;
segmentation; tumor region
INTRODUCTION
Medical imaging plays a central role in the diagnosis of brain tumors. Brain
tumor diagnosis is quite difficult because of diverse shape, size, location and
appearance of tumor in brain.
The brain tumor detection is very hard in beginning stage
because it cannot find the accurate measurement of tumor.
Thus, the brain tumor detection still remains challenging
problem due to complex structureof brainthanotherbreast,
heart, lung, kidney, bone cancer detections in medical fields.
tation from MRI data is an important process
consuming and difficult task often performed
manually by medical experts. Radiologists andothermedical
experts spend a substantial amount of time segmenting
MRI brain images
are analyzed. Moreover,accuratelylabeling braintumormay
lead to missing diagnosis. And it is a considerable variation
that is observed between doctors. To avoid human based
diagnostic error,computeraideddiagnosis systemis needed.
Throughout the few years, different segmentation methods
have been used for tumor detection but it is time consuming
process and also gives inaccurate result. And most of them
fail because of unknown noises, poor image contrast, in
ndaries that are usualinmedical
images. In this paper, the Brain Tumor Detection System is
designed to detect the brain tumor from MRI brain images
accurately in order to enhance the performanceoftheimage
segmentation especially in the field of brain tissue
Implementation and identification of brain tumor are done
by segmentation and detecting thebrainimagefromtheMRI
processing of the image is the
primary step which removes noises and smoothes the input
brain image. And, segmentation is carried out using
thresholding technique. Then, the brain tumor region is
detected from the segmented image using region properties.
Finally, the area and location of the brain tumor are
calculated. The purpose of the sys
radiologists who examine and determine the symptoms of
brain tumor in biomedical field by using image processing
techniques. Therefore, once it gets identified brain tumor, it
gives to start the proper treatment
and it may be curable.
RELATED WORK
This session presents the related work for the detection of
brain tumor in digital image processing.
K., Surabhi [1] have proposed an efficient brain tumor
detection system based on the segmentation technique for
MRI brain images, which can detect tumor and trace the
position of tumor in the MRI brain images. To detect the
tumor region, segmentation method is used o
threshold and it exactly detects the tumor region withbetter
accuracy. In their proposed work,somepre
(resizing, binarization and morphological operations) are
used for detection of tumor region in MRI brain images.
Their study introduces an efficient detection of brain tumor
from MRI brain images based on the segmentation of gray
matter and white matter. The purpose of this segmentation
algorithm is to label tumor objects within the MRI brain
image and locate their region
tumor is extracted from the MRI brain images and its exact
position is also determined to classify the volume of tumor.
By experimental analysis of various parameters such as
PSNR, MSE and volume or percentage of tumor, they
observed that the accuracy of their proposed systemis more
that 90% and it is better than the previous work.
International Journal of Trend in Scientific Research and Development (IJTSRD)
-ISSN: 2456 – 6470
August 2019 Page 2070
or MRI Image
f Mechatronic Engineering,
Today, computer aided system is widely used in various fields. Among them,
the brain tumor detection is an important task in medical image processing.
brain tumors plays an important role in improving
treatment possibilities and increases the survival rate of the patients. Manual
segmentation of brain tumors for cancer diagnosis, from large amount of
Magnetic Resonance Imaging (MRI) images generated in clinical routine, is a
difficult and time consuming task or even generates errors. So, the automatic
brain tumor segmentation is needed to segment tumor. The purpose of the
thesis is to detect the brain tumor quickly and accurately from the MRI brain
In the system, the average filter is used to remove noise and make
smooth an input MRI image and threshold segmentationis appliedto segment
tumor region from MRI brain images. Region properties method is used to
, the equation of the tumor region in
the system is effectively applied in any shape of the tumor region.
area of tumor; brain tumor detection; location in brain;MRIimage;
central role in the diagnosis of brain tumors. Brain
tumor diagnosis is quite difficult because of diverse shape, size, location and
Finally, the area and location of the brain tumor are
calculated. The purpose of the systemis toassistdoctors and
radiologists who examine and determine the symptoms of
brain tumor in biomedical field by using image processing
techniques. Therefore, once it gets identified brain tumor, it
gives to start the proper treatment
This session presents the related work for the detection of
brain tumor in digital image processing.
[1] have proposed an efficient brain tumor
detection system based on the segmentation technique for
MRI brain images, which can detect tumor and trace the
position of tumor in the MRI brain images. To detect the
tumor region, segmentation method is used on the basis of
threshold and it exactly detects the tumor region withbetter
accuracy. In their proposed work,somepre-processingsteps
(resizing, binarization and morphological operations) are
used for detection of tumor region in MRI brain images.
study introduces an efficient detection of brain tumor
from MRI brain images based on the segmentation of gray
matter and white matter. The purpose of this segmentation
algorithm is to label tumor objects within the MRI brain
image and locate their region based onthewhitematter.The
tumor is extracted from the MRI brain images and its exact
position is also determined to classify the volume of tumor.
By experimental analysis of various parameters such as
PSNR, MSE and volume or percentage of tumor, they have
observed that the accuracy of their proposed systemis more
that 90% and it is better than the previous work.](https://image.slidesharecdn.com/395braintumordetectionsystemformriimage-190917124354/85/Brain-Tumor-Detection-System-for-MRI-Image-1-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD27864 | Volume – 3 | Issue – 5 | July - August 2019 Page 2071
Devkota, et al. [2] have proposed a computeraideddetection
approach to diagnose brain tumor in its early stage using
Mathematical Morphological Reconstruction (MMR). Image
is pre-processed to remove noise and artifacts and then
segmented to find regions of interest withprobabletumor.A
large number of textural and statistical features are
extracted from the segmented image to classify whether the
brain tumor in the image is benign or malignant. Their
experimental results showthat thesegmented images havea
high accuracy while substantially reducing the computation
time. Their study shows that the proposed solution can be
used to diagnose brain tumor in patients with a high success
rate.
A., Meena, and K., Raja proposed an approach of Spatial
Fuzzy C-Means (PETSFCM) clustering algorithmonPositron
Emission Tomography (PET) scan image datasets.
The algorithm is joining the spatial neighborhood
information with classical Fuzzy C-Means (FCM) and
updating the objective function of each cluster. Spatial
relationship of neighboring pixel is an aid of image
segmentation.Theseneighboringpixels arehighlyrenovated
the same feature data. In spatial domain, the member-ships
of the neighbor centered are specified to obtain the cluster
distribution statistics. They calculated the weighting
function based on these statistics and applied into the
member-ship function. Their algorithm is tested on data
collection of patients with Alzheimer’s disease. They did not
calculate objective based quality assessment that could
analyze images and did not report their quality without
human involvement.
METHODOLOGY
This session expresses the background theory used in the
brain tumor detection system. Average filter, image
segmentation, region properties and calculation of tumor
area in MRI brain images are explained in the following
sections.
A. Average Filter
The default filter type is the average or mean filter in the
general class. The average filter is low pass filter. Theoutput
cell value calculated by filter is the simple average
(arithmetic mean) of the cells in the filter window. The
averaging performed by the average filter removes some of
the higher frequency features, while allowing the low-
frequency features to pass through the filter unchanged
(thus the term “low pass” filter). This has the effect of
smoothing the raster image, emphasizing its larger-scale
brightness trends. Whether the smoothing produced by the
low pass filter is beneficial or not depends on the
characteristics of the input image. Average filter is used to
smooth the image and remove noisessuchas Gaussiannoise.
It reduces the amount of intensity variation between one
pixel and the next.
Average filter is an estimation of average from neighbor
pixels within the kernel. The filtering equation can be
expressed as (1).
g(x,y) = 1/M∑(x,y)∈S f(x,y) (1)
where S is the neighborhood of pixel (x,y), M is the number
of pixels in neighborhood S, and g(x,y) is the filtered image.
Sub-images are summed up and then multiplied by 1/M [4].
B. Image Segmentation
Thresholding is one of the commonly used methods for
image segmentation [5]. Image threshold is a simple,
effective and way of partitioning an image into a foreground
and background. This image analysis technique is a type of
image segmentation that isolates objects by converting
grayscale images into binary images.Regions ofanimage are
separated out corresponding to objects which want to
analyze. Each pixel is labeled as belonging to one of
(typically) two classes. At present, threshold-based methods
are classified into global, local andadaptivethresholdings. In
this study, global threshold based segmentation method is
used to segment brain tumor in MRI brain image.
Global (single) thresholding method is used when the
intensity distribution between the objects offoreground and
background are very distinct. When the differences between
foreground and background objects are very distinct, a
single value of threshold can simply be used to differentiate
both objects apart [6]. Global methods finda singlethreshold
value T for the whole document. Then each pixel is assigned
to page foreground or background based on its gray value
comparing with thethresholdvalue.Globalmethods arevery
fast and they give good results for typical scanned
documents. For many years, the binarization of a grayscale
document was based on the global thresholding statistical
algorithms [7]. Somemostcommonusedglobalthresholding
methods are Otsu method, entropy based thresholding, etc.
Otsu’s algorithm is a popular global thresholding technique.
Moreover, there are many popular thresholding techniques
such as Kittler and Illingworth, Kapur, Tasi, Huang, Yen and
et al [8]. The condition for selecting the T is given as (2).
g(x, y) =
1 if f(x, y) > T
0 otherwise
(2)
where g(x, y) is the thresholded image, f (x, y) is the input
image, and T is the threshold value. Equation (2) has no
indication on selecting the threshold value T. The threshold
T separates the object from the dark background. Any point
(x,y) for which f(x, y) >T is called an object point. Pixels are
labeled 1 corresponds to object whereas pixels labeled 0
corresponds to the background [8]. Global thresholding is
popular due to simplicity and easy implementation [5][9].
C. Region Properties
Image regions are also called objects, connected
components, or blobs, can be contiguous or discontiguous.It
returns measurements for the set of properties specified by
properties for 8-connected component(object) inthebinary
image, black and white. A region in an image can have
properties, such as Area, BoundingBox, ConvexArea,
Centroid, Eccentricity, ConvexHull, EquivDiameter,
EulerNumber, Extent, Extrema, FilledArea, Image,
FilledImage, MajorAxisLength, MinorAxisLength,
Orientation, PixelList, PixelIdxList, Solidity, SubarrayIdx,
Perimeter, ConvexImage. Measurement values are returned
as an array of structure or a table. Region properties canbea
comma-separated list of strings, a cell array containing
strings, the single string 'all', or the string 'basic'. If
properties are the string 'all', then all the preceding
measurements are computed. If properties are not specified
or are the string 'basic', then these measurements are
computed: 'Area', 'Centroid', and 'BoundingBox' [10].](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)