Back close

Automatic Segmentation Employing Fuzzy Connectedness

Publication Type : Journal Article

Publisher : International Review on Computers and Software

Source : International Review on Computers and Software, Praise Worthy Prize, FEDERICO II University 21 Claudio Naples I 80125 Italy, Volume 7, Issue 2, p.561-567 (2012)

Url :

Keywords : Algorithms, Automatic segmentations; Brain images; Data sets; Fuzzy affinity; Fuzzy C-means algorithms; Fuzzy connectedness, Computer vision, Fuzzy connectedness, Fuzzy connectedness algorithm, Fuzzy systems, Fuzzy theory, Graphic methods, Histogram thresholding, Image processing and computer vision, Image segmentation, Medical images, Natural images, Research topics, Seed point, Segmentation, Similarity coefficients, Tissue

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2012

Abstract : Image segmentation is an important research topic of image processing and computer vision community since long. The variety and complexity of images and applications of segmentation make this problem a challenging and critical one. Algorithms based on fuzzy theory have found to provide improved accuracy of segmentation. Fuzzy schemes for segmentation employ fuzzy connectedness to capture the fuzziness associated with the measure of 'hanging togetherness'. The proposed work is a fully automatic segmentation method based on fuzzy affinity and fuzzy connectedness. This uses histogram thresholding to automatically select the salient seed points of segments before applying the fuzzy connectedness algorithm. Performance of the proposed approach is compared with Fuzzy C Means (FCM) algorithm employing sensitivity, specificity and dice similarity coefficient for the cases of natural image data set and medical image data set. The proposed algorithm exhibits superior performance figures for both the data sets when compared to Fuzzy C means algorithm. It is also observed that, unlike FCM, the algorithm treats small size spots as separate regions or segments which is very important in brain image segmentation to locate lesions from the rest of the tissues. This property of the algorithm provides increased accuracy for brain image segmentation. © 2012 Praise Worthy Prize S.r.l.

Cite this Research Publication : Jyothisha J. Nair and Govindan, V. K., “Automatic Segmentation Employing Fuzzy Connectedness”, International Review on Computers and Software, vol. 7, no. 2, pp. 561-567, 2012

Admissions Apply Now