Second Order Fuzzy Measure and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images

Authors:
Pradipta Maji;Malay K. Kundu;Bhabatosh Chanda
Affiliations:
(Correspd.) Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. E-mail:{pmaji,malay}@isical.ac.in;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. E-mail:{pmaji,malay}@isical.ac.in;Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India. E-mail:chanda@isical.ac.in
Venue:
Fundamenta Informaticae
Year:
2008

Citing 8
Cited 2

A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Index of area coverage of fuzzy image subsets and object extraction

Pattern Recognition Letters
Higher order fuzzy entropy and hybrid entropy of a set

Information Sciences: an International Journal
Image segmentation using fuzzy correlation

Information Sciences: an International Journal
Digital Picture Processing

Digital Picture Processing
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets

Fundamenta Informaticae
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image segmentation by histogram thresholding using fuzzy sets

IEEE Transactions on Image Processing

Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

Transactions on Rough Sets IX
Improving feature space based image segmentation via density modification

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A robust thresholding technique is proposed in this paper for segmentation of brain MR images. It is based on the fuzzy thresholding techniques. Its aim is to threshold the gray level histogram of brain MR images by splitting the image histogram into multiple crisp subsets. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through a second order fuzzy measure such as fuzzy correlation, fuzzy entropy, and index of fuzziness. To calculate the second order fuzzy measure, a weighted co-occurrence matrix is presented, which extracts the local information more accurately. Two quantitative indices are introduced to determine the multiple thresholds of the given histogram. The effectiveness of the proposed algorithm, along with a comparisonwith standard thresholding techniques, is demonstrated on a set of brain MR images.