Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness

This paper presents a Fuzzy C-Means based image segmentation approach with an optimum threshold using measure of fuzziness. The optimized version, herein denoted as FCM-t, benefits from an optimum threshold, calculated using measure of fuzziness. This allows the revealing of ambiguous pixels, which are eventually assigned to the appropriate clusters by calculating the rounded average cluster values in the ambiguous pixels neighbourhood. The proposed approach showed significantly better results compared to the traditional Fuzzy C-Means, at the cost of some processing power. By benefiting from the optimum threshold approach, one is able to increase the segmentation performance by approximately three times more than with the traditional FCM.

[1]  Qing Wang,et al.  Image Thresholding by Maximizing the Index of Nonfuzziness of the 2-D Grayscale Histogram , 2002, Comput. Vis. Image Underst..

[2]  Abdul Rahman Ramli,et al.  Survey on liver CT image segmentation methods , 2011, Artificial Intelligence Review.

[3]  R. Shanmugalakshmi,et al.  Fundamentals of Digital Image Processing , 2006 .

[4]  Luigi Cinque,et al.  Image thresholding using fuzzy entropies , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[5]  M S van Leeuwen,et al.  Focal liver lesions: characterization with triphasic spiral CT. , 1996, Radiology.

[6]  Xiaolei Huang,et al.  Medical Image Segmentation , 2009 .

[7]  I. Vernersson Open University Press , 2000 .

[8]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[9]  J. Peacock Two-dimensional goodness-of-fit testing in astronomy , 1983 .

[10]  Nikhil R. Pal,et al.  Fuzzy divergence, probability measure of fuzzy events and image thresholding , 1992, Pattern Recognit. Lett..

[11]  L. Zadeh Calculus of fuzzy restrictions , 1996 .

[12]  T. Pavlidis,et al.  Fuzzy sets and their applications to cognitive and decision processes , 1977 .

[13]  E. Sreenivasa Reddy,et al.  Tizhoosh ’ s Fuzzy membership function To measure the image fuzziness , 2012 .

[14]  Joseph D. Bronzino,et al.  The Biomedical Engineering Handbook , 1995 .

[15]  Mao-Jiun J. Wang,et al.  Image thresholding by minimizing the measures of fuzzines , 1995, Pattern Recognit..

[16]  A. K. Ray,et al.  Segmentation using fuzzy divergence , 2003, Pattern Recognit. Lett..

[17]  Sankar K. Pal,et al.  Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation , 2008, Trans. Rough Sets.

[18]  Gunnar Läthén Blood vessels , multi-scale filtering and level set methods , 2010 .

[19]  Aboul Ella Hassanien,et al.  Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[20]  Hamid R. Tizhoosh,et al.  Image thresholding using type II fuzzy sets , 2005, Pattern Recognit..