Segmentation of medical images using Simulated Annealing Based Fuzzy C Means algorithm

被引：18

作者：

Sharma, Neeraj ^{[1
]}

Ray, Amit K. ^{[1
]}

Sharma, Shiru ^{[1
]}

Shukla, K. K. ^{[2
]}

Aggarwal, Lalit M. ^{[3
]}

Pradhan, Satyajit ^{[3
]}

机构：

[1] Banaras Hindu Univ, Inst Technol, Sch Biomed Engn, Varanasi 221005, Uttar Pradesh, India

[2] Banaras Hindu Univ, Inst Technol, Dept Comp Engn, Varanasi 221005, Uttar Pradesh, India

[3] Banaras Hindu Univ, Inst Med Sci, Dept Radiotherapy & Radiat Med, Varanasi 221005, Uttar Pradesh, India

来源：

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY | 2009年 / 2卷 / 03期

关键词：

medical images; texture features; clustering; SA; simulated annealing; segmentation;

D O I：

10.1504/IJBET.2009.024422

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Accurate segmentation is desirable for analysis and diagnosis of medical images. This study provides methodology for fully automated simulated annealing based fuzzy c-means algorithm, modelled as graph search method. The approach is unsupervised based on pixel clustering using textural features. The virtually training free algorithm needs initial temperature and cooling rate as input parameters. Experimentation on more than 180 MR and CT images for different parameter values, has suggested the best-suited values for accurate segmentation. An overall 97% correct segmentation has been achieved. The results, evaluated by radiologists, are of clinical importance for segmentation and classification of Region of Interest.

引用

页码：260 / 278

页数：19

共 27 条

[1] Simulated annealing using a Reversible Jump Markov Chain Monte Carlo algorithm for fuzzy clustering [J].