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 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. This paper reviews the processes and techniques used in detecting tumor
based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and
magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors
almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.
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
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
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
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
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. This paper reviews the processes and techniques used in detecting tumor
based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and
magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors
almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.
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
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
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
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
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
AN EFFECTIVE AND EFFICIENT FEATURE SELECTION METHOD FOR LUNG CANCER DETECTIONijcsit
Medical image data is growing rapidly. Lung cancer considers to be the most common cause of death among people throughout the world. Early lung cancer detection can increase the chance of people survival. The 5 year survival rate for lung cancer patient increases from 14 to 49% if the disease is detected in time. Computed Tomography can be more efficient than X ray for detecting lung cancer in time. But the problem seemed to merge due to time constraint in detecting the presence of lung cancer.MAT LAB have been applied for the study of these techniques. Feature selection is a method to reduce the number of features in medical applications where the image has hundreds or thousands of features. In order to extract the accurate features of an image, an image need to be processed for its effective retreival.Image feature selection is an essential task for recognizing the image and it can be done for overcoming classification problems. However, the quality of the image recognition tasks can be improved with the help
of better classification accuracy for enhancing the retrieval performance.
Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Brain tumor detection and localization in magnetic resonance imagingijitcs
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 detectionand 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.
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.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
AN EFFECTIVE AND EFFICIENT FEATURE SELECTION METHOD FOR LUNG CANCER DETECTIONijcsit
Medical image data is growing rapidly. Lung cancer considers to be the most common cause of death among people throughout the world. Early lung cancer detection can increase the chance of people survival. The 5 year survival rate for lung cancer patient increases from 14 to 49% if the disease is detected in time. Computed Tomography can be more efficient than X ray for detecting lung cancer in time. But the problem seemed to merge due to time constraint in detecting the presence of lung cancer.MAT LAB have been applied for the study of these techniques. Feature selection is a method to reduce the number of features in medical applications where the image has hundreds or thousands of features. In order to extract the accurate features of an image, an image need to be processed for its effective retreival.Image feature selection is an essential task for recognizing the image and it can be done for overcoming classification problems. However, the quality of the image recognition tasks can be improved with the help
of better classification accuracy for enhancing the retrieval performance.
Recognition of brain cancer and cerebrospinal fluid due to the usage of diffe...journalBEEI
Medicinal images assume an important part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. Similarly, MRI could be the cutting-edge regenerative imaging technology that allows for a sectional angle perspective of the body that gives specialists convenience and will inspect the person-concerned. In this paper, the author has attempted the strategy to classify MRI images at the beginning of production to have a tumor or recognition. The study aims to address the aforementioned problems associated with brain cancer with a CSF leak. This research, the author focuses on brain tumor and applies the statistical model for the testing and also discusses the images of a brain tumor. They can judge the tumor region by conducting a comparative image analysis and applying Histogram function afterwards to construct a classifier that could be prepared to predict tumor and non-tumor MRI examinees based on the support vector machine. Our system is capable of detecting the right region that a pathologist also highlights. In the future, this should be more driven with the objective that tumors can be arranged and describe the solution in the medical terms & implementation with gives some predictions about the future generated by modified technology.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Brain tumor detection and localization in magnetic resonance imagingijitcs
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 detectionand 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.
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.
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
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
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.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
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.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Lung cancer is one of the leading
causes of mortality in every country, affecting
both men and women. Lung cancer has a low
prognosis, resulting in a high death rate. The
computing sector is fully automating it, and the
medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
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 Survey on Segmentation Techniques Used For Brain Tumor DetectionEditor IJMTER
In recent years Brain tumor is one of the most commonly found causes for death among
children and adults. Early detection of tumor is a must in order to reduce the death rate. For tumor
detection various image techniques can be used. In this paper we mainly concentrate on the images
obtained from MRI scans. In MRI images, the tumor may appear clearly, but for further treatment
the physician need to be a qualified and well experienced person. In order to help the radiologist in
detection computer-aided diagnosis was developed. The generation of a CAD system consists of
several processes and among them segmentation is considered to the most important process. Image
Segmentation is a process of partitioning an image into multiple segments. The main objective of
segmentation is to represent the image into a simplified form so as to increase the efficiency and
accuracy of the system. Therefore the segmentation of brain tumor can be considered as an important
role in the medical image process. Hence in this paper we concentrate on the recently used
segmentation techniques for the detection of tumor using MRI images.
Similar to Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vision (20)
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
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In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
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Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
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Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vision
1. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
DOI : 10.14810/ijscmc.2014.3201 1
GLIOBLASTOMA MULTIFORME IDENTIFICATION
FROM MEDICAL IMAGING USING COMPUTER
VISION
Ed-Edily M. Azhari, Mudzakkir M. Hatta, Zaw Zaw Htike and Shoon Lei Win
Faculty of Engineering, IIUM, Kuala Lumpur, Malaysia
ABSTRACT
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.
KEYWORDS
Tumor Detection, Medical Imaging, Computer Vision, Machine Learning
1. INTRODUCTION
Based on statistics, tumors are the second cause of cancer-related deaths in children (both males
and females) whose are under the age of 20 and in males whose age 20 to 39 [1-5,27]. This
disease is also the fifth leading cause of cancer-related deaths in females ages 20-39. This facts
increase the importance of the researches on the tumor detection and this will present the
opportunity for doctors to help save lives by detecting the disease earlier and perform necessary
actions. Varieties of image processing techniques are available to be applied on various imaging
modalities for tumor detection that will detect certain features of the tumors such as the shape,
border, calcification and texture. These features will make the detection processes more accurate
and easier as there are some standard characteristics of each features for a specific tumor [6-7].
All tumors will start small and grow with time. As they grow, they will become more
conspicuous and increase the probability of showing their characters. A person with tumor
2. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
2
usually has certain symptoms and this will bring that person to a physician. From this, they will
be able to detect the smallest possible symptomatic malignant (cancerous) tumors that is in early
stage and the smallest possible asymptomatic tumors in the screening process. Basically, there are
many factors that can influence the appearance of tumors in different kind of processed images
despite some common features of malignancies because of variation in the type of tissue and
tumor. For large tumor, characteristic features often to be found while in small tumors, these
features of malignancy do not appear to be many and some of them might represent themselves
by secondary effects such as distortion in its architecture [8-12].
In the case of a suspected tumor, “a doctor may perform a neurologic exam to determine if the
patient's senses, reflexes, mental status and memory are working normally. The doctor may also
order imaging tests, including computed tomography (CT) or magnetic resonance imaging (MRI)
of the brain, to pinpoint the tumor and show its size.”[13]
Imaging technology has progressed immensely in recent years. Different kind of images can be
produced just by a single-click and different image processing techniques can be done to these
images to study in detail about it. Machine learning and machine vision technology has also been
used to solve numerous problems in medicine [20-22,32-41]. Image-based tumor detection uses
one or more algorithms as the primary modeling. Some can detect edges, some can detect shapes
while others can detect other features. With advances in camera sensing and computational
technologies, advances in tumor detection using these features have been an extremely active
research area in the intelligent medical community. Clearly, recent researches and trials have
extremely help in advancing diagnostic tools for medical purposes but still, the fact that gains in
survival need to be achieved by better diagnostic tools [14, 15].
1.1. Magnetic Resonance Imaging (MRI)
MRI is commonly used in the medical field for detection and visualization of details in the
internal structure of the body. It is basically used to detect the differences in the body tissues
which have a considerably better technique as compared to computed tomography [23]. Thus, this
technique become a special technique especially for the brain tumor detection and cancer imaging
[23]. Basically, for comparison, CT uses ionizing radiation while MRI uses strong magnetic field
to align the nuclear magnetization that follows by changes the alignment of the magnetization by
radio frequencies that can be detected by the scanner. The signal produced can be further
processed later to gain extra information of the body [23].
This paper provide a review of image-based tumor detection. The authors then review image-
based tumor detection, commenting on techniques applied for color detection and shape
detection. They provide their insights and perspectives on future research directions in image-
based tumor detection.
2. BACKGROUND ON TUMOR DETECTION
Nowadays, brain tumor has become one of the main cause for increasing mortality among
children and adults [23]. Based on some researches, it has been found that the number of people
suffering and dying from brain tumors has been increased to 300 per year during past few decades
[23]. Figure 1 shows the incidence of brain tumor in various age groups.
3. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
3
Figure 1: Brain tumor age distribution
Figure 2 shows the 5-year survival rates over the past three decades. It shows that the survival
rates have improved with the advancement in imaging and diagnosis technology.
Figure 2: 5-Year brain tumor survival rate over years [24]
According to Nagalkar and Asole [16], CT-Scan technique usually used for monitoring the
images of damaged brain part. The images of the CT Scans are shown in the form of gray scale
images as the equipment for CT scans support this form of image color and for easy detection of
tumor from the image [16]. For example, in the parietal section of the head scanned using CT
scans, the Cerebrum part is shown in the form of the gray color while the veins and arteries parts
in the form of creamish white color [16]. Any clotting that exist in the brain that show any kind of
damage can be detected as dark gray in color. The process of extraction of parameters are
basically like taking out per pixel information and then plotting it [16]. For an image obtained
from CT-Scan, the images are shown in this manner; tumor appears white and brain damaged
cells shown in black color, thus the binary values of the pixel showing the brain damaged cells
are 0 and showing the tumor are 1, thus by extraction process, further analysis can be done such
as checking and plotting in MATLAB [16]. The patient with damaged brain can be differentiated
from normal patient by using this technique. In addition, tumor can also be detected clearly based
on the image results [16].
4. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
4
Figure 3: Brain CT Scan image a) Normal patient b) Tumor patient [16]
Figure 4: Basic flowchart to determine existence of tumor [16]
Additionally, this technique has been practiced to determine tumor patient’s response to treatment
since long time ago. The radiologist has made series of cross-sectional diameter measurements
for indicator lesions purposes by using axial, incremental CT image data. Later, these
measurements will be compared with the previous measurement scans [25]. Nevertheless, the
measurement of lesion diameter does not represent the exact assessment of tumor size due to
some factors, such as:
Irregular lesions
The lesions that grow other than sphere shape may not adequately represented by
diameters’ changes;
Different measurement between inter-observer and intra-observer
Referred to the image selection used for the measurement and the location of lesion
boundary;
Different levels of scanning results collected from various diagnoses
The lesions may not be captured exactly at the same spot from one diagnosis to another.
Hence, it affects the lesion’s image in which causes comparisons between examinations
becoming more difficult.
5. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
5
On the other hand, with the ability to provide single breath-hold scans and overlapping
reconstructions, the spiral CT is capable to improve the image lesions at reproducible levels from
different diagnosis [25]. Even so, these lesions’ measurements are still a subjective task and yet
open for variability as described in previous factors (a) and (b). What follows is a summary of
several approaches to the problems involved in nodule detection. Therefore, the researchers have
divided these approaches into two groups of methods which are [25]:
(a) The process of detecting the pulmonary vasculature in order to help the detection of
pulmonary nodules (if vasculature is successfully removed from the image, then only
nodules should remain).
(b) The process of detecting objects in the CT image volume to distinguish between
nodules and other structures.
3. PROPOSED METHODOLOGY
3.1. Image Acquisition
Images obtained or used should be of MRI scans and these scanned images are displayed in a two
dimensional matrices which will have the number of pixels as its elements. Images are stored in
Matlab and converted (if not already) to be displayed as a gray scale image of size 256*256 [23].
The size is important to reduce processing time or to be large enough to be considered for proper
processing. The values of the gray scale image would range from 0 to 255, where 0 represents
total black color and 255 shows pure white color [23]. Anything in between shows a variety of
values representing the intensities of gray color [18].
Figure 7: Block diagram of the Proposed Tumor Detection
3.2. Pre-processing
Pre-processing generally means removing noise and improving or altering image quality to suit a
purpose. For this study, only commonly used enhancement and noise reduction techniques were
implemented [23]. The image enhancement that the study is interested in should yield the result
of more prominent edges and a sharpened image, noise will be reduced thus reducing the blurring
or salt paper effect from the image that might produce errors [23].
After image enhancement, image segmentation will be applied. This step is vital as the improved
and enhanced image will yield better results when detecting edges and improving the quality of
the overall image. Edge detection will lead to determining and understanding the outline shape of
the tumor. The following steps will be exemplified in the preprocessing stage [23]:
6. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
6
(a) Noise Removal: there is a wide range of filters available to be used to remove the noise
from the images. Linear filters present on Matlab with simple line of code can also serve
the purpose such as Gaussian and averaging filters. Salt and pepper noise is a common
noise present in original captured images. Average filters for example, can remove these
noise but with the sacrifice of sharpness of image. Median filter is also another example
of a filter used to remove the noise like salt and pepper. In the median filter value of pixel
is determined by the median of the values of its neighboring pixels [17]. This filter
however, is less sensitive to the outliers [23].
(b) Any filter able to remove the noise present in the original image could be used for this
purpose. However, in tumor detection, the sharpness of the edges, obtained from the
sudden change of intensity, is a focal point and should be kept preserved. The next step
will help improve the sharpness of the edges [23].
(c) Image Sharpening: sharpening is generally achieved by using high pass filters. After
applying low pass filters (noise removing step), we now need to sharpen the image to
ensure edges are kept. This is important as edges will detect and highlight the tumor for
us. Gaussian filter (a high pass filter) is used to enhance the boundaries of the objects. It
is widely used and the paper proposes to imitate [23].
3.3. Edge Detection
Edge detection is the most vital part in tumor detection. It is used to determine the boundaries of
the object. In this step, canny edge filter is used.
“Five steps in canny filter: 1.Filters out noise in original image 2.Smoothing the image using
filters 3.Finding the edge directions with library of orientation 4. Relate to a direction that can be
traced in an image 5.After edge directions are known, non-maximum suppression is done to thin
out of remove edges based on threshold values determined.” [18]
The two main purpose of using canny filter is [30]:
1. Control the amount of detail which appears in the edge image.
2. Suppress noise.
3.4. Modified Histogram Clustering
Histogram is one of the powerful techniques in image enhancement. The histogram of an image
represents the relative frequency of occurrence of the various gray levels based on their individual
values [28]. This is useful in stretching low-contrasting images. Clustering in which similar
neighbored bins are grouped together and finally thresholding is set in order to detect the tumor.
One of the method in histogram clustering is the histogram values are plotted and the threshold
value is fixed based on the gray level values and pixels in the image. To obtain this, we first plot
the values and take a guess on the best value to use. The white parts of the image will have the
maximum value of 255 and the black parts will translate to the value zero. Based on the values of
the tumor, (certain studies keep first guess values to be at 128 as threshold value) the tumor image
is processed. The values should be tested for various data sets of tumor images to identify the
closest threshold value and the results are displayed later. Certain researches have specified that
beyond the 132 mark, tumor is not detected clearly anymore [17]. Later, the tumor part is
extracted via the morphological operators by taking and specifying the region of interest (ROI)
and displaying it clearly.
7. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
7
3.5. Morphological Operators
Morphological operation involves dilation and erosion. Dilation combines two sets using vector
addition. Erosion combines two sets using vector subtraction and is the dual operator of dilation
[31]. Both are not invertible transforms.
Morphological techniques called "opening-by-reconstruction" and "closing-by-reconstruction" to
"clean" up the image is used. These operations will create a gap between the high and low
intensity objects and enhance it to locate images. The enhanced maxima inside each object that
can be located using imregionalmax [26].
Opening is an erosion followed by a dilation, while opening-by-reconstruction is an erosion
followed by a morphological reconstruction [18, 26].
4. EXPERIMENTS
The data is first presented according to example pictures of the image processes involved in the
system. As discussed in the previous design part, the image used is of grayscale MRI brain scans.
First we acquire the images and pre-processes it under a median filter. These are some of the
examples the images:
Figure 8: Images before (left) and after (right) median filter is applied
Next the edges of the images are specified with the sobel edge detection. The images processed
are as follows:
Figure 9: Images after sobel edge detection algorithm is applied
8. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
8
Later the images would go through a Modified Histogram Clustering - Color Threshold technique
to identify the position of the tumor. According to various MRI scan images, the threshold values
of the image’s HSV for the tumor happens to be common and lies between these values:
Table 1. Threshold values applied
HSV High (%) Low (%)
Hue 35 0
Saturation 75 0
Value 100 70
The values are set as a threshold and applied with convolution with the original image to
determine the location of the tumor.
Figure 10: After applying modified threshold clustering technique
Through the syntax of bwlabel() on Matlab, each block of image found is subjected to a value of
label. This allows us to gain property information about the block using the improps() syntax. The
area can be found and displayed.
Figure 11: Display of results
Next we segment out the tumor subject from the original image. For this purpose, we use a
watershed technique. The technique uses a series of algorithm that allows segmentation of high
color contrast. The result of the imposed technique is as follows:
9. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
9
Figure 12: Watershed segmentation technique outcome
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 proposed system achieved an
error rate of 8% in identifying and localizing tumors.
We used a median filter for preprocessing because it yields a noise removed image that is still
closely similar to the original image. In tumor detection, tampering too much with the intensity of
color and contrast may lead to less accurate segmentation. This is because the tumor detection
technique used in this study mainly focuses on the color contrast of tumors as its main feature.
According to previous studies, the median filter is also the most useful filter for MRI scan images
because it deals best with salt and pepper noise without tampering too much with the color
contrast.
In the color clustering technique, the intensity of the grey color contrast in tumor and the
background brain sets the threshold value. This technique is used mainly because the color
contrast is the only recognizable feature of a tumor. Even doctors rely solely on this feature to
detect tumor from MRI scans.
Watershed segmentation is an important part of the project. It uses an opening-closing
reconstruction technique which is basically the enhancing and dimming of foreground and
background images. The contrast is then elevated. Reconstructing would highlight the high
intensity objects. The reason for using this method is because tumor is detected according to grey
color contrast or intensity. Watershed technique is suitable as it segments based on this feature.
According to the experiments and results obtained, the system records an 8% error. These errors
are solely false positive errors. This means that images without tumors end up with detected
blobs. The errors usually happen when the original images supplied are not grayscale images. The
noise of the RGB or HSV conversion might have produced the error.
Figure 14: Error in modified threshold clustering
10. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 2, May 2014
10
The shows the detection of the skull as a form of tumor as well. Therefore it detects the patient to
have a metastasis level tumor or a cancer of level 2 or above.
Figure 15: Error in detection
From the same error example as before, it shows that the program recognizes 3 areas of tumor.
These areas are the example of the false positive errors recorded.
5. CONCLUSION
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. This system achieved an error rate of 8%. 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.
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