Image Segmentation Algorithms on 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.

Download Full-text

A New Image Segmentation Method Based on Three-Dimensional Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.157 ◽

2012 ◽

Vol 490-495 ◽

pp. 157-161

Author(s):

Guo Fu Lin

Keyword(s):

Image Segmentation ◽

Classification Accuracy ◽

Probabilistic Approach ◽

Three Dimensional ◽

Training Method ◽

Segmentation Method ◽

Brain Images ◽

Feature Sets ◽

Mr Brain Images ◽

Mr Brain

In this paper, a three-dimensional probabilistic approach for MR brain image segmentation is proposed. Based on the noise-free representative reference vectors provided by SOM, the results of the 3D-PNN method are superior to other traditional algorithms. In addition to the 3D-PNN architecture, a fast three-step training method is proposed. The proposed approach also incorporates structure tensor to find appropriate feature sets for the 3D-PNN with respect to resulting classification accuracy. Computational results with simulated MR brain images have shown the promising performance of the proposed method.

Download Full-text

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

Download Full-text

Modified Expectation Maximization Method for Automatic Segmentation of MR Brain Images

10.54294/you4rl ◽

2013 ◽

Author(s):

Meena prakash R ◽

Shantha Selva Kumari R

Keyword(s):

Image Segmentation ◽

Spatial Correlation ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Expectation Maximization ◽

Gaussian Mixture ◽

Brain Images ◽

Brain Image ◽

Mr Brain Images ◽

Mr Brain

An automated method of MR Brain image segmentation is presented. A block based Expectation Maximization method is presented for the tissue classification of MR Brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain Image Segmentation and Expectation Maximization algorithm is used to estimate the model parameters. The Gaussian Mixture Model considers each pixel as independent and does not take into account the spatial correlation between the neighbouring pixels. Hence the segmentation result obtained using standard GMM is highly sensitive to Inensity Non-Uniformity and noise. The image is divided into blocks before applying EM since the GMM is preserved in the local image blocks. Also, Nonsubsampled Contourlet Transform is employed to incorporate the spatial correlation among the neighbouring pixels. The method is applied to the 12 MR Brain volumes of MRBRAINS13 test data and the White Matter, Gray Matter and CSF structures were segmented.

Download Full-text

Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2015070103 ◽

2015 ◽

Vol 6 (3) ◽

pp. 33-48 ◽

Cited By ~ 2

Author(s):

Ouarda Assas

Keyword(s):

Image Segmentation ◽

Fuzzy Sets ◽

Brain Images ◽

Image Thresholding ◽

Mr Brain Images ◽

Mr Brain ◽

Optimum Threshold ◽

Selection Of

Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset of standard grayscale test images and MR Brain images. Experiment results show that the type-2 Fuzzy 2-partition entropy algorithm performs equally well in terms of the quality of image segmentation and leads to a good visual result.

Download Full-text