Volumetric Analysis of Abnormal Region in MR Brain Images

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.

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Analysis of Segmentation Algorithms for Detection of Anomalies in MR Brain Images

Lecture Notes in Electrical Engineering - Advances in Communication Systems and Networks ◽

10.1007/978-981-15-3992-3_11 ◽

2020 ◽

pp. 129-146

Author(s):

A. Lenin Fred ◽

S. N. Kumar ◽

Parasuraman Padmanabhan ◽

Balazs Gulyas ◽

H. Ajay Kumar

Keyword(s):

Brain Images ◽

Segmentation Algorithms ◽

Mr Brain Images ◽

Mr Brain

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Comparison of edge-based and ridge-based registration of CT and MR brain images

Medical Image Analysis ◽

10.1016/s1361-8415(96)80010-7 ◽

1996 ◽

Vol 1 (2) ◽

pp. 151-161 ◽

Cited By ~ 58

Author(s):

J.B.Antoine Maintz ◽

Petra A. van den Elsen ◽

Max A. Viergever

Keyword(s):

Brain Images ◽

Mr Brain Images ◽

Edge Based ◽

Mr Brain

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Brain Image Segmentation with Gradient Information

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.38.27882 ◽

2018 ◽

Vol 7 (4.38) ◽

pp. 1392

Author(s):

Sri Purwani ◽

Julita Nahar ◽

Carole Twining

Keyword(s):

Ground Truth ◽

Real Data ◽

Gaussian Mixture ◽

Previous Method ◽

Brain Images ◽

Brain Image ◽

Gradient Information ◽

Segmentation Methods ◽

Mr Brain Images ◽

Mr Brain

Segmentation is the process of extracting structures within the images. The purpose is to simplify the representation of the image into something meaningful and easier to analyse. A magnetic resonance (MR) brain image can be represented as three main tissues, e.g. cerebrospinal fluid (CSF), grey matter and white matter. Although various segmentation methods have been developed, such images are generally segmented by modelling the intensity histogram by using a Gaussian Mixture Model (GMM). However, the standard use of 1D histogram sometimes fails to find the mean for Gaussians. We hence solved this by including gradient information in the 2D intensity and intensity gradient histogram. We applied our methods on real data of 2D MR brain images. We then compared the methods with the previous published method of Petrovic et al. on their dataset, as well as on our larger datasets extracted from the same database of 3D MR brain mages, where the ground-truth annotations are available. This shows that our method performs better than the previous method.

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Tissue Segmentation Methods Using 2D Histogram Matching in a Sequence of MR Brain Images

New Approaches in Intelligent Image Analysis - Intelligent Systems Reference Library ◽

10.1007/978-3-319-32192-9_6 ◽

2016 ◽

pp. 183-222

Author(s):

Vladimir Kanchev ◽

Roumen Kountchev

Keyword(s):

Tissue Segmentation ◽

Brain Images ◽

Histogram Matching ◽

Segmentation Methods ◽

Mr Brain Images ◽

Mr Brain ◽

2D Histogram

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8790.088619 ◽

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

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