A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

Agrawal, Sanjay; Panda, Rutuparna; Dora, Lingraj

doi:10.1016/j.asoc.2014.08.011

Cited by 44 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Standardization of global intensity scale to a local intensity scale is necessary for further segmentation process (Zhung and Udupa 2009). Many researchers have analyzed brain MRI segmentation using FSs, see Zhao et al (2013), Agrawal et al (2014). But these algorithms still have problems due to various situations, for example, capturing brain images under poor illumination make it uncertain.…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy clustering algorithm for the segmentation of brain tumor

2015

View full text Add to dashboard Cite

This paper introduces a new method of clustering algorithm based on interval-valued intuitionistic fuzzy sets (IVIFSs) generated from intuitionistic fuzzy sets to analyze tumor in magnetic resonance (MR) images by reducing time complexity and errors. Based on fuzzy clustering, during the segmentation process one can consider numerous cases of uncertainty involving in membership function, distance measure, fuzzifier, and so on. Due to poor illumination of medical images, uncertainty emerges in their gray levels. This paper concentrates on uncertainty in the allotment of values to the membership function of the uncertain pixels. Proposed method initially pre-processes the brain MR images to remove noise, standardize intensity, and extract brain region. Subsequently IVIFSs are constructed to utilize in the clustering algorithm. Results are compared with the segmented images obtained using histogram thresholding, k-means, fuzzy c-means, intuitionistic fuzzy c-means, and interval type-2 fuzzy c-means algorithms and it has been proven that the proposed method is more effective.Keywords Brain MR image · Segmentation · Intervalvalued intuitionistic fuzzy set · Brain tumor · Clustering Communicated by V. Loia.

show abstract

Section: Introductionmentioning

confidence: 99%

A new fuzzy clustering algorithm for the segmentation of brain tumor

2015

View full text Add to dashboard Cite

show abstract

“…(Ahmed et al, 2002; Siyal and Yu, ; Zhao et al, ; Feng et al, 2013; Li and Shen, ; Ramathilagam et al, ; Ji et al, ; Chen et al, ). A method of using Soft Computing Approaches to compute the cluster centers of FCM segmentation is proposed (Agrawal et al, ). A weighted membership function which incorporates local and global spatial information is introduced to overcome the effects of sensitivity to noise and intensity inhomogeneity is proposed (Adhikari et al, ).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Prakash

Kumari

2016

Int. J. Imaging Syst. Technol.

View full text Add to dashboard Cite

This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1-weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.

show abstract

“…[3] In this research presents a novel technique of intracranial segmentation of magnetic resonance (MR) brain image pertaining to pixel intensity values by optimum boundary point detection by values (OBPD) method. The recently proposed (OBPD) method comprises of three steps.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

A Brief Review of Image Segmentation Based on Thresholding Technique

Dilip¹

2017

IJRASET

View full text Add to dashboard Cite

Abstract:Image processing plays an important role in computer vision. The process of image segmentation provides the partition of image into different segments per their feature attribute. Image segmentation divided into two types based on similarity and based on discontinuity. Region based segmentation is a type similarity based segmentation. Another type of segmentation is called thresholding based segmentation. In thresholding based segmentation method some thresholding techniques are used. Moreover, technologies required for one field of image may not suitable for other types of image. Many image applications can be found on binary or gray image but almost none for color image segmentation. In this paper, we present the study of various image segmentation techniques.

show abstract

A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches

Cited by 44 publications

References 19 publications

A new fuzzy clustering algorithm for the segmentation of brain tumor

A new fuzzy clustering algorithm for the segmentation of brain tumor

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

A Brief Review of Image Segmentation Based on Thresholding Technique

Contact Info

Product

Resources

About