International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
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.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
Brain Tumor Detection using MRI ImagesYogeshIJTSRD
Brain tumor segmentation is a very important task in medical image processing. Early diagnosis of brain tumors plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. For the study of tumor detection and segmentation, MRI Images are very useful in recent years. One of the foremost crucial tasks in any brain tumor detection system is that the detachment of abnormal tissues from normal brain tissues. Because of MRI Images, we will detect the brain tumor. Detection of unusual growth of tissues and blocks of blood within the system is seen in an MRI Imaging. Brain tumor detection using MRI images may be a challenging task due to the brains complex structure.In this paper, we propose an image segmentation method to detect tumors from MRI images using an interface of GUI in MATLAB. The method of distinguishing brain tumors through MRI images is often sorted into four sections of image processing as pre processing, feature extraction, image segmentation, and image classification. During this paper, weve used various algorithms for the partial fulfillment of the necessities to hit the simplest results that may help us to detect brain tumors within the early stage. Deepa Dangwal | Aditya Nautiyal | Dakshita Adhikari | Kapil Joshi "Brain Tumor Detection using MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology - 2021 , May 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42456.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/42456/brain-tumor-detection-using-mri-images/deepa-dangwal
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Clustering of medline documents using semi supervised spectral clusteringeSAT Journals
Abstract We are considering: local-content (LC) information, global-content (GC) information from PubMed and MESH (medical subject heading-MS) for the clustering of bio-medical documents. The performances of MEDLINE document clustering are enhanced from previous methods by combining both the LC and GC. We propose a semi-supervised spectral clustering method to overcome the limitations of representation space of earlier methods. Keywords- document clustering, semi-supervised clustering, spectral clustering
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.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
A SIMPLE IMAGE PROCESSING APPROACH TO ABNORMAL SLICES DETECTION FROM MRI TUMO...ijma
This paper proposed a method for brain tumor detection from the magnetic resonance imaging (MRI) of
human head scans. The proposed work explained the tumor detection process by means of image
processing transformations and thresholding technique. The MRI images are preprocessed by
transformation techniques and thus enhance the tumor region. Then the images are checked for
abnormality using fuzzy symmetric measure (FSM). If abnormal, then Otsu’s thresholding is used to extract
the tumor region. Experiments with the proposed method were done on 17 datasets. Various evaluation
parameters were used to validate the proposed method. The predictive accuracy (PA) and dice coefficient
(DC) values of proposed method reached maximum.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
INVESTIGATION THE EFFECT OF USING GRAY LEVEL AND RGB CHANNELS ON BRAIN TUMOR ...csandit
Analysis the effect of using gray level on the Brain tumor image for improving speed of object
detection in the field of Medical Image using image processing technique. Specific areas of
interest are image binarization method, Image segmentation. Experiments will be performed by
image processing using Matlab. This paper presents a strategy for decreasing the calculation
time by using gray level and just one channel Red or Green or Blue in medical Image and
analysis its impact in order to improve detection time and the main goal is to reduce time
complexity.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TR...ijfcstjournal
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP).
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
Cars are a very important part of this modern world because they give luxury and comfort. Even
though they are comfortable, some problems always keep arising on the safety side. After a lot of research they
rectified certain problems using air bags, auto parking, turbo charger, pedal shift…, etc.
And now we are going to discuss about one such problem that arises on the safety side. An unsuspected
accident occurs when people smash their fingers in between the car doors. Due to this kind of accident around
120,000 people are injured every year. But this was not taken as a very major safety concern for the customer.
To avoid this kind accident due to car doors, we are introducing “SAFETY DOOR LOCK SYSTEM”
with the help of “HYDRAULIC PISTON AND IR SENSORS”.
The major working process of the “SAFETY DOOR LOCK SYSTEM”is, when a person places his/her
hand or fingers in the gap between the door and the outer panel, at the time when the closing action of the door
takes place, the Sensors start to transmit the Infra Red Rays to the Receivers at the
other end, and so even if someone closes the door without anybody‟s knowledge the hydraulic piston will
automatically come out and stop the door from closing and prevent the person from the unsuspected accident
and minor injuries by the car door and ensure maximum safety to the customer.
Extrusion can be defined as the process of subjecting a material to compression so that it is forced to
flow through an opening of a die and takes the shape of the hole. Multi-hole extrusion is the process of
extruding the products through a die having more than one hole. Multi-hole extrusion increases the production
rate and reduces the cost of production. In this study the ram force has calculated experimentally for single hole
and multi-hole extrusion. The comparison of ram forces between the single hole and multi-hole extrusion
provides the inverse relation between the numbers of holes in a die and ram force. The experimental lengths of
the extruded products through the various holes of multi-hole die are different. It indicates that the flow pattern
is dependent on the material behavior. The micro-hardness test has done for the extruded products of lead
through multi-hole die. It is observed that the hardness of the extruded lead products from the central hole is
found to be more than that of the products extruded from other holes. The study suggests that multi-hole
extrusion can be used for obtaining the extruded products of lead with varying hardness. The micro-structure
study has done for the lead material before and after extrusion. It is observed that the size of grains of lead
material after extrusion is smaller than the original lead.
Analysis of Agile and Multi-Agent Based Process Scheduling Modelirjes
As an answer of long growing frustration of waterfall Software development life cycle concepts,
agile software development concept was evolved in 90’s. The most popular agile methodologies is the Extreme
Programming (XP). Most software companies nowadays aim to produce efficient, flexible and valuable
Software in short time period with minimal costs, and within unstable, changing environments. This complex
problem can be modeled as a multi-agent based system, where agents negotiate resources. Agents can be used to
represent projects and resources. Crucial for the multi-agent based system in project scheduling model, is the
availability of an effective algorithm for prioritizing and scheduling of task. To evaluate the models, simulations
were carried out with real life and several generated data sets. The developed model (Multi-agent based System)
provides an optimized and flexible agile process scheduling and reduces overheads in the software process as it
responds quickly to changing requirements without excessive work in project scheduling.
Effects of Cutting Tool Parameters on Surface Roughnessirjes
This paper presents of the influence on surface roughness of Co28Cr6Mo medical alloy machined
on a CNC lathe based on cutting parameters (rotational speed, feed rate, depth of cut and nose radius).The
influences of cutting parameters have been presented in graphical form for understanding. To achieve the
minimum surface roughness, the optimum values obtained for rpm, feed rate, depth of cut and nose radius were
respectively, 318 rpm, 0,1 mm/rev, 0,7 mm and 0,8 mm. Maximum surface roughness has been revealed the
values obtained for rpm, feed rate, depth of cut and nose radius were respectively, 318 rpm, 0,25 mm/rev, 0,9
mm and 0,4 mm.
Possible limits of accuracy in measurement of fundamental physical constantsirjes
The measurement uncertainties of Fundamental Physical Constants should take into account all
possible and most influencing factors. One from them is the finiteness of the model that causes the existence of
a-priori error. The proposed formula for calculation of this error provides a comparison of its value with the
actual experimental measurement error that cannot be done an arbitrarily small. According to the suggested
approach, the error of the researched Fundamental Physical Constant, measured in conventional field studies,
will always be higher than the error caused by the finite number of dimensional recorded variables of physicalmathematical
models. Examples of practical application of the considered concept for measurement of fine
structure constant, speed of light and Newtonian constant of gravitation are discussed.
Performance Comparison of Energy Detection Based Spectrum Sensing for Cogniti...irjes
With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing
demand for wireless radio spectrum. However, the policy of fixed spectrum assignment produces a bottleneck for more
efficient spectrum utilization, such that a great portion of the licensed spectrum is severely under-utilized. So the concept of
cognitive radio was introduced to address this issue.The inefficient usage of the limited spectrum necessitates the
development of dynamic spectrum access techniques, where users who have no spectrum licenses, also known as secondary
users, are allowed to use the temporarily unused licensed spectrum. For this purpose we have to know the presence or
absence of primary users for spectrum usage. So spectrums sensing is one of the major requirements of cognitive radio.Many
spectrum sensing techniques have been developed to sense the presence or absence of a licensed user. This paper evaluates
the performance of the energy detection based spectrum sensing technique in noisy and fading environments.The
performance of the energy detection technique will be evaluated by use of Receiver Operating Characteristics (ROC) curves
over additive white Gaussian noise (AWGN) and fading channels.
Comparative Study of Pre-Engineered and Conventional Steel Frames for Differe...irjes
In this paper, the conventional steel frames having triangular Pratt truss as a roofing system of 60 m
length, span 30m and varying bay spacing 4m, 5m and 6m respectively having eaves level for all the portals is at
10m and the EOT crane is supported at the height of 8m from ground level and pre-engineered steel frames of
same dimensions are analyzed and designed for wind zones (wind zone 2, wind zone 3, wind zone 4 and wind
zone 5) by using STAAD Pro V8i. The study deals with the comparative study of both conventional and preengineered
with respect to the amount of structural steel required, reduction in dead load of the structure.
Flip bifurcation and chaos control in discrete-time Prey-predator model irjes
The dynamics of discrete-time prey-predator model are investigated. The result indicates that the
model undergo a flip bifurcation which found by using center manifold theorem and bifurcation theory.
Numerical simulation not only illustrate our results, but also exhibit the complex dynamic behavior, such as the
periodic doubling in period-2, -4 -8, quasi- periodic orbits and chaotic set. Finally, the feedback control method
is used to stabilize chaotic orbits at an unstable interior point.
Energy Awareness and the Role of “Critical Mass” In Smart Citiesirjes
A Smart City could be depicted as a place, logical and physical, in which a crowd of heterogeneous
entities is related in time and space through different types of interactions. Any type of entity, whether it is a
device or a person, clustered in communities, becomes a source of context-based data.
Energy awareness is able to drive the process of bringing our society to limit energy waste and to optimize
usage of available resources, causing a strong environmental and social impact. Then, following social network
analysis methodologies related to the dynamics of complex systems, it is possible to find out, emergent and
sometimes hidden new habits of electricity usage. Through an initial Critical Mass, involving a multitude of
consumers, each related to more contexts, we evaluate the triggering and spreading of a collective attitude. To
this aim, in this paper, we propose a novel analytical model defining a new concept of critical mass, which
includes centrality measures both in a single layer and in a multilayer social network.
A Firefly Algorithm for Optimizing Spur Gear Parameters Under Non-Lubricated ...irjes
Firefly algorithm is one of the emerging evolutionary approaches for complex and non-linear
optimization problems. It is inspired by natural firefly‟s behavior such as movement of fireflies based on
brightness and by overcoming the constraints such as light absorption, obstacles, distance, etc. In this research,
firefly‟s movement had been simulated computationally to identify the best parameters for spur gear pair by
considering the design and manufacturing constraints. The proposed algorithm was tested with the traditional
design parameters and found the results are at par in less computational time by satisfying the constraints.
The Effect of Orientation of Vortex Generators on Aerodynamic Drag Reduction ...irjes
One of the main reasons for the aerodynamic drag in automotive vehicles is the flow separation
near the vehicle’s rear end. To delay this flow separation, vortex generators are used in recent vehicles. The
vortex generators are commonly used in aircrafts to prevent flow separation. Even though vortex generators
themselves create drag, but they also reduce drag by delaying flow separation at downstream. The overall effect
of vortex generators is more beneficial and proved by experimentation. The effect depends on the shape,size and
orientation of vortex generators. Hence optimized shape with proper orientation is essential for getting better
results.This paper presents the effect of vortex generators at different orientation to the flow field and the
mechanism by which these effects takes place.
An Assessment of The Relationship Between The Availability of Financial Resou...irjes
The availability of financial resources is an important element in impacting the success of a planning
process for an effective physical planning. The extent to which however, they are articulated in the process
remained elusive both in scholarly and public discourse. The objective of this study wastherefore, to examine
the extent to which financial resources affect physical planning. In doing so, the study examinedwhether
financial resources were adequate or not to facilitate planning processes in Paidha. According to the study
findings,budget prioritization and ceilings are still a challenge in Paidha Town Council. This is partly due
limited level of knowledge of physical planning among the officials of Paidha Town Council. As a result, there
were no dedicated budget line for routine inspection of physical development plan compliance and enforcement
tools in Paidha. In conclusion, in addressing uncoordinated patterns of physical development that characterize
Uganda‟s urban centres, a critical starting point ought to be the analysis of physical planning process. The
research of this kind is not only significant to other emerging urban centres facing poor a road network,
mushrooming informal settlements and poor social services including poor pattern of residential and commercial
developments but also to all institutions that are involved in planning these towns. Knowing the extent of need
for financial influences in planning may assist local authorities to take the processes of planning seriously which
will help enhance the sustainable development of emerging urban centres including Paidha.
The Choice of Antenatal Care and Delivery Place in Surabaya (Based on Prefere...irjes
- Person's desire to do a pregnancy examination is determined by the service place that suits the tastes
and facilities owned by it. Until now, the utilization of antenatal care by pregnant women is still low (Mardiana,
2014). The purpose of the study is to analyze factors affecting the utilization of antenatal care and delivery place
in Surabaya city based on the preferences and choice theory.
Type of survey research is cross sectional approach, the population is mothers who have children aged 1-
12 months in Surabaya. The large sample of 250 mothers who have children aged 1-12 months in 2013 is taken
by simple random sampling technique. Variables of the research are the preference elements and steps, choice
elements and steps, utilization of antenatal care and delivery place. Data were collected through questionnaires
and secondary data were then analyzed with descriptive statistics in the form of a frequency distribution, shown
by the schematic diagram.
The result showed that the preference elements and steps showed almost half (42.9%) desire to give birth
in a health care because of information got from someone else, while the choice element and step shows the
bulk (57.1%) of the criteria of delivery place chosen is a safe, comfortable and cheap delivery place, the labor
place which is the main choice most (57.1%) is cheap, comfortable, close.
Conclusion of the research based on the preferences and choice theory can be found three (3) new
theories, they are preferences become choice, preferences do not become choice, choice is preceded by
preferences
Wave Transmission on Submerged Breakwater with Interlocking D-Block Armor
D232430
1. International Refereed Journal of Engineering and Science (IRJES)
ISSN (Online) 2319-183X, (Print) 2319-1821
Volume 2, Issue 3(March 2013), PP.24-30
www.irjes.com
Artefact Removal from MRI of Brain Image
Sudipta Roy, Kingshuk Chatterjee, Indra Kanta Maitra, Prof. Samir Kumar Bandyopadhyay
Department of Computer Science and Engineering, University of Calcutta,
92 A.P.C. Road, Kolkata-700009, India.
Abstract: Many different artefacts can occur during magnetic resonance imaging(MRI), some affecting the
diagnosticquality, while others may be confusedwith pathology. Thus to detect any abnormalities in brain like
tumor, edema artefact must be removed otherwise it will treated as an abnormality in automated system or may
hamper the intelligence system. This paper proposed a methodology to remove the artefacts from MRI of brain.
The proposed methodology is very simple with the combination of statistical and computational geometric
approach. Statistical methods like standard deviation are used to calculate the global threshold to binarize the
image and computational geometry like convex hull is used to produce final output (MRI without artefact).
Keywords: MRI of Brain, Artefact, Standard Deviation, Global Thresholding, Convex Hull.
I. Introduction
Magnetic Resonance Imaging is considered very powerful diagnostic methods to detect any
abnormalities. As in all imaging process, artefacts can occur, resulting in degraded quality of image which can
compromise imaging evaluation. An artefact is a feature appearing in an image that is notpresent in the original
object. Artefacts remain a problematic area inmagnetic resonance imaging (MRI) and some affect the quality of
the examination,while others may be confusedwith pathology. Depending on their origin, artefacts are typically
classified as patient-related, signal processing dependent and hardware (machine)-related. Pre-processing (
artefact removal) techniques are used to improve the detection of the suspicious region from Magnetic
Resonance Images (MRI). Thus a statistical method has been served to remove the artefact from MRI of brain
image and the proposed method has been successfully implemented and produces very good results. This
process helps to diagnosis any disease from MRI of brain.We no longer look to MR imaging to provide only
structural information, but also functional information of various kinds such that information about blood flow,
cardiac function, biochemical processes, tumor kinetics, and blood oxygen levels (for mapping of brain
function).
The rest of this paper is organized as follow: in section 2,description of some other artefact removal
methodology; after that in the section 3, proposed methodology has been described; and section 4 describe
results by proposed methods; in section 5 failure estimation has been described; finally, the conclusion part has
been describe in the section 6.
II. Brief Review:
The large growth in MR imaging field is attributable to rapid technological advances in several areas,
including magnet technology, gradient coil design, radiofrequency (RF) technology, and computer engineering.
In stride with the rapid technological advances, there has been phenomenal growth in the number of applications
for MR imaging. Artefacts remain a problematic area in magnetic resonance imaging (MRI). Depending on their
origin, artefacts are typically classified as patient-related, signal processing dependent and hardware related. L J
Erasmus [1] (2004) et. al. gives a very good description of different type of MRI artefacts. Bradley G. Goodyear
et. al. in 2004 [2] proposed a technique that based on the Stockwell transform (ST), a mathematical operation
that provides the frequency content at each time point within a time-varying signal. Using this technique, 1D
Fourier transforms (FTs) are performed on raw image data to obtain phase profiles and results; navigator echo
correction is successful at removing phasefluctuations due to physiological processes such as respiration.The ST
filter, on the other hand, does not perform well nor is it designed to alter phase oscillations at such low
frequencies.Qing X. Yang [3] in Removal of Local Field Gradient Artefacts in T2 Weighted Images at High
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3. Artefact Removal from MRI of Brain Image
brain are separate and if ratio is high then the skull and brain are in 1 component and the 2nd highest component
is an artefact as the artefact occupies less area therefore the ratio is high. Therefore based on the ratio value we
keep the maximum component or the maximum component and the second maximum component. Thus a
binarized image without artefact is produced. To produce final output the convex hull of all one pixels in the
binarized image is obtained. Then all pixels inside the convex hull of binarized image are set to one and the
binarized image matrix is multiplied position wise to the original image to obtain MR image without artefacts.
Convex hull is used for reducing metal related susceptibilityand Gibbs artefact. Here Quickhull Algorithm for
Convex Hulls is used. Quickhull Algorithm [13] for Convex Hulls runs faster when the input contains non-
extreme points and it uses merged facets to guarantee that the output is clearly convex.
3.1 Algorithm (pseudo code):
Step 1. Grayscale MRI brain image is taken as input.
Step 2.Threshold value of the image is calculated using the standard deviation technique described above.
Step 3. The image is binarized using the threshold value. i.e. pixels having value greater than the threshold is set
to 1 and pixels less than the threshold are set to 0.
/* [A B]=size(I);
BI =zeros(a,b);
STD = std2(I);
FORi = 1 to A DO
FOR j=1 to B DO
IF I(i,j) > STD THEN
Set BI(i,j)=1
END IF
END FOR
END FOR*/
Step 4. The binarized image is labelled and areas of connected components are calculated using equivalence
classes.
Step 5. The connected component with the maximum area and the connected component with the second highest
area are found out.
Step 6. The ratio of the maximumarea to that of second maximum area are calculated if the ratio is high
(signifies that the skull are brain are together as one component as explained above) and if ratio is low (signifies
the skull and brain are two different component as explained above).
Step 7. On the basis of the ratio if ratio is high only the component with highest area is kept and all others are
removed otherwise if ratio is low the component with the highest and second highest area are kept and all others
are removed.
Step 8. A convex hull is calculated for the one pixel in the image and all regions within the convexhull are set to
one.
Step 9.Now the above obtained image matrix is multiplied to the original image matrix to obtain an image
consisting of only brain and skull and without any artefact.
3.1.1 Correctness
Loop invariant: At start of every iteration of outer loop, each row of image i = 1, 2, . . . . . . , A
and inner loop , each column j = 1, 2, . . . . . . , B; in the end of the iteration it follows same
process.
Initialization: Since i = 1 i.e. it is at first row of the image before the first iteration of the outer loop, so,
the invariant is initially true and is same for the inner loop; some variable are initialize
with the image size and dimension which is finite.
Maintenance: In each successive iteration loop invariant moves to next row by incrementing loop
variable. Loop works by moving Image [i + 1][j], Image [i + 2][j], Image [i + 3][j] and so on.
Termination: The outer loop ends when outer loop >height, i.e. all the row of the image is already
traversed.
3.1.2 Complexity Analysis
Assuming the height = width =n, the running time for both for loop is O(n × n) for all cases;At each
level of the recursion, partitioning requires O(n) time. If partitions were guaranteed to havea size equal to a
fixed portion, and this held at each level,the worst case time would be O(n log n). However, those criteria do not
apply; Partitions may have size in O(n) (they are not balanced). Hence the worst case running time is O(n2).
Thus if we consider worst case time: O(n 2) and expected time: O(n log n). To calculate the area of each
component it requires O(n × n) running time.T(n)= O(n2) + O(n log n) + O(n2) = O(n2).
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4. Artefact Removal from MRI of Brain Image
IV. Results and Discussion:
Now a day’s artefacts are principally letter or metal related artefacts or Gibbs artefact. Letter artefact is
present in most of the brain MRI images due to patient’s information being embedded in them. High quality of
MRI machine ensures metal related and susceptibility artefact are very few. Thus according to the proposed
methods here three output are given below. Original MRI image is shown in figure1 (A) and corresponding
binary image is shown in figure1 (B), in this image global threshold value is selected by the standard deviation
of the image. This binarized image separates the background and foreground part of the MRI of image which
helps to remove the artefact from the MRI image. Removing artefact by calculating each component and the
binarized output is shown in figure1 (C). Maximum number of artefacts (mainly letter) removed from this step
but if any metal or Gibbs artefact are present then those are removed by applying Quickhull convex hull and
shown in figure1(D). In figure 1(D) no artefact present i.e. all artefacts are removed by proposed algorithms.
Some other results; input corresponding output are shown in appendix section.
(A) (B)
(C) (D)
Figure 1:(A) is the input MRI of brain image with artefact; (B) is the binarized output by global thresholding
method using standard deviation approach; (C) is the binarized output without major artefact; (D) is the
desired output image without any artefacts.
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5. Artefact Removal from MRI of Brain Image
V. Failure estimation
If any artefact (principally letter or metal artefact) is above the brain portion or any connected artefact
with the original brain portion then proposed methods fails. This is because when area of the connected
components calculates then artefact component may be treated as brain component along with brain component
and a proposed algorithm does not succeed.
VI. Conclusion:
Artefact removal from MRI of brain tumor is a pre-processing step for detecting any abnormalities of
MRI of brain. Here intelligence system for artefact removal on MRI of brain has been implemented. The
automated system remove artefact using low time complexity. Proposed methods are based on a combination of
statistical and geometric methods and it give very good results for different kinds of MRI of brain images.
Proposed methods tested on large dataset and produce excellent results except connected artefact with the
original brain portion image. The results show that the new method can overcome the shortcomings of the
previous methods and improve the artefact removal methodology in the sense of brain abnormalities detection.
Reference:
[1] L J Erasmus , D Hurter , M Naudé , H G Kritzinger , S Acho , “A short overview of MRI artefacts”, SA Journal Of Radiology,
August, 2004.
[2] Bradley G. Goodyear, Hongmei Zhu, Robert A. Brown, and J. Ross Mitchell, “Removal of Phase Artefacts From fMRI Data Using a
Stockwell Transform Filter Improves Brain Activity Detection”, Magnetic Resonance in Medicine 51:16–21 (2004)
[3] Qing X. Yang, Gerald D. Williams, Roger J. Demeure, Timothy J. Mosher, Michael, B. Smith, “Removal Of Local Field Gradient
Artefacts In T2 Weighted Images At High Fields By Gradient Echo Slice Excitation Profile Image”, Magnetic Resonance In
Medicine, 1996.
[4] Philip J. Allen, Oliver Josephs, Robert Turner, “A Method for Removing Imaging Artefact from Continuous EEG Recorded during
Functional MRI”Neuro Image 12, 230–239 (2000).
[5] Travis B Smith, Krishna S Nayak“ MRI artefacts and correction strategies”, Imaging Med. (2010) 2(4), 445–457
[6] D. Mantini, M.G. Perrucci, S. Cugini, A. Ferretti, G.L. Romani, and C. Del Gratta,“Complete artefact removal for EEG recorded
during continuous fMRI using independent component analysis”, NeuroImage 34 (2007) 598–607.
[7] S Roy and S K Bandyopadhyay, “Detection and Quantification of Brain Tumor from MRI of Brain and it’s Symmetric
Analysis”International Journal of Information and Communication Technology Research(IJICTR), pp. 477-483,Volume 2, Number
6, June 2012.
[8] S Roy, A Dey, K Chatterjee, Prof. S K. Bandyopadhyay, "An Efficient Binarization Method for MRI of Brain Image”, Signal &
Image Processing : An International Journal (SIPIJ) Vol.3, No.6,pp. 35-51 December 2012.
[9] K. Selvanayaki , P. Kalugasalam , “Performance Evaluation of Genetic Algorithm Segmentation on Magnetic Resonance Images for
Detection of Brain Tumor”, European Journal of Scientific Research, Vol. 86 No 3 September, 2012, pp.365-378
[10] Ian T. Young ,Jan J. Gerbrands , Lucas J. van Vliet, “Fundamentals of Image Processing”, Version 2.3, pp-1-112.
[11] Haralick, Robert M., and Linda G. Shapiro, Computer and Robot Vision, Volume I, Addison-Wesley, 1992, pp. 28-
[12] www.mathworks.com
[13] C. Bradford Barber, David P. Dobkin, HannuHuhdanpaa, “The Quickhull Algorithm for Convex Hulls”, ACM Transactions on
Mathematical Software, Vol. 22, No. 4, December 1996, Pages 469–483.
[14] S Roy, A Saha, and Prof. S K Bandyopadhyay. “Brain Tumor Segmentation And Quantification From Mri Of Brain”, Journal of
Global Research in Computer Science(JGRCS), Volume 2, No. 4, April 2011.
Appendix:
(I1) (O1)
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7. Artefact Removal from MRI of Brain Image
(I4) (O4)
Figure 2 : I1, I2, I3, I4 are the input MRI of brain image with artefact and O1,O2, O3, O4 are the corresponding
output MRI of brain image without any artefact.
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