scholarly journals Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2881-2887
Keyword(s):  
Fuzzy Clustering ◽  
Bias Field ◽  
Mr Images ◽  
Brain Images ◽  
Normal Tissues ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Abnormal Tissue

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

scholarly journals Interpolation of the Histogramed MR Brain Images for Resolution Enhancement

2019 ◽  
Vol 8 (11) ◽  
pp. 1253-1256 ◽  
Keyword(s):  
Performance Metrics ◽  
Interpolation Method ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
The Body ◽  
Mr Images ◽  
Brain Images ◽  
Method Performance ◽  
Mr Brain Images ◽  
Mr Brain

Magnetic resonance imaging (MRI) is an incredible testing method which provides appropriate anatomical images of the body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. However, with the measured MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. Usually to improve the resolution of the MR image, histogram equalization process has to be applied. In this paper, interpolation method is applied to improve the resolution of MR brain images for the histogram-ed images. And for the assessment of the skillfulness of introduced method, performance metrics such as peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE) are measured. The peak of signal for the enhanced images through interpolation will be much better and may have the good variation to the mean brightness error. With this there can be potential to the artificial intelligence for better diagnosis in complex decisive instances

scholarly journals Parameterization of BFO Algorithm for the Improved Functionality of MFKM Technique for Better Pathological Identification in Brain MR Image

2019 ◽  
Vol 9 (2S2) ◽  
pp. 956-961
Keyword(s):  
Volume Effect ◽  
High Accuracy ◽  
Medical Image Segmentation ◽  
Brain Images ◽  
Normal Tissues ◽  
Mr Brain Images ◽  
To Come ◽  
High Level ◽  
Mr Brain

Intensity inhomogeneity, high level of noise, partial volume effect and poor image contrast are the major artefacts in medical image segmentation. Any of these artefacts might lead to unclear boundaries of tissues, hence the segmentation of tissues in the MR brain image cannot be determined with high accuracy, and this would be a problem to the radiologists to diagnose or to start the treatment because of the lack of facility to operate over the brain in in-vivo condition. This makes the radiologist and surgeons/experts to take time to come for the conclusion on pathology of a particular patient. So, the radiologists and experts need to give more exertion when this condition is applied for many patients at a day, to diagnose and to start treatment. To make this effortless to them, also for accurate diagnosis, this research paper provides an robust algorithm using the Modified Fuzzy K-Means (MFKM) and Bacteria Foraging Optimization (BFO) algorithm, which segments the abnormal tissues among the normal tissues from MR brain images with high accuracy. The accuracy of the Improved MFKM (IMFKM) algorithm is obtained in terms of Sensitivity and Specificity, and the proposed algorithm proves better segmentation results than the other conventional algorithms.

scholarly journals Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

2021 ◽  
Vol 7 (2) ◽  
pp. 763-766
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Network Performance ◽  
Neurodegenerative Disorder ◽  
Automated Analysis ◽  
Brain Regions ◽  
Mr Images ◽  
Residual Network ◽  
Brain Images ◽  
Precise Diagnosis ◽  
Mr Brain Images ◽  
Mr Brain

Abstract Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

Deep learning based enhanced tumor segmentation approach for MR brain images

2019 ◽  
Vol 78 ◽  
pp. 346-354 ◽  
Author(s):  
Mamta Mittal ◽  
Lalit Mohan Goyal ◽  
Sumit Kaur ◽  
Iqbaldeep Kaur ◽  
Amit Verma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tumor Segmentation ◽  
Brain Images ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain

An Efficient Computational Approach for the Detection of MR Brain Tissues in the Presence of Noise and Intensity Inhomogeneity

2017 ◽  
Vol 33 ◽  
pp. 65-79 ◽  
Author(s):  
G. Sandhya ◽  
Kande Giri Babu ◽  
T. Satya Savithri
Keyword(s):  
Energy Minimization ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Brain Images ◽  
Brain Image ◽  
Anisotropic Diffusion Filter ◽  
The Brain ◽  
Mr Brain ◽  
Brain Tissues

The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization process called as Anisotropic Multiplicative Intrinsic Component Optimization (AMICO) for the brain image segmentation in the presence of noise and intensity inhomogeneity to separate different tissues. This algorithm uses an efficient Anisotropic diffusion filter to decrease the noise. The denoised image gets segmented after the correction of intensity inhomogeneity by the MICO algorithm. The algorithm decomposes the MR brain image as two multiplicative intrinsic components, called as the component of the true image which represents the physical properties of the brain tissue and the component of bias field that is related to intensity inhomogeneity. By optimizing the values of these two components using an efficient energy minimization technique, correction of intensity inhomogeneity and segmentation of the tissues can be achieved simultaneously. Performance evaluation and the comparison with some existing methods have validated the remarkable performance of AMICO in terms of efficiency of segmentation of brain images in the presence of noise and intensity inhomogeneity.

scholarly journals Volumetric Analysis of Abnormal Region in MR Brain Images

2019 ◽  
Vol 9 (1) ◽  
pp. 5129-5137
Keyword(s):  
Imaging Modality ◽  
Complex Structure ◽  
Volumetric Analysis ◽  
Brain Images ◽  
Depth Measurement ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Mr Brain Images ◽  
Edge Based ◽  
Mr Brain

MRI is known as one of the best imaging modality used for neuro image analysis. Detection of abnormality regions in Brain image is critical due to its complex structure, which can be accurately analyzed with MRI. Several methods and segmentation algorithms have been proposed in the past to extract the abnormal region however there is further scope of increasing the segmentation efficiency. In this work abnormality region in brain is extracted with region based and edge based hybrid segmentation methods and thus obtained region is rendered for volumetric analysis. This analysis is used for depth measurement and localization of abnormal region accurately. Apart from this analysis mainly provides the information about the abnormal region distribution and its connectivity with other regions.

scholarly journals Differentiation of Alzheimer Conditions in MR Brain Images Using a Single Inception Module Network

2021 ◽  
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Input Image ◽  
Mr Images ◽  
Brain Images ◽  
Medical Expertise ◽  
Module Network ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Normal Controls

In this study, an attempt has been made to differentiate Alzheimer’s Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network. The performance of the model is computed and analyzed for different input image sizes. Results show that the single inception module is able to classify AD stages using MR images. The end-to-end network differentiates AD from NC with 85% precision. The model is found to be effective for varied sizes of input images. Since the proposed approach is able to categorize AD stages, single inception module networks could be used for the automated AD diagnosis with minimum medical expertise.

scholarly journals Image Segmentation Algorithms on MR Brain Images

2013 ◽  
Vol 67 (16) ◽  
pp. 18-20
Author(s):  
G. EvelinSuji ◽  
Y. V. S. Lakshimi ◽  
G. Wiselin Jiji
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Segmentation Algorithms ◽  
Mr Brain Images ◽  
Mr Brain
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