Subscribe Now Subscribe Today
Science Alert
 
Blue
   
Curve Top
Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 6 | Page No.: 587-592
DOI: 10.3923/jas.2012.587.592
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Automatic Tissue Segmentation in Medical Images using Differential Evolution
K. Karteeka Pavan, V. Sesha Srinivas, A. SriKrishna and B. Eswara Reddy

Abstract: Segmentation of medical images is a challenging task and preprocessing step in medical diagnosis. Evolutionary algorithms such as Genetic Algorithms (GA) have been found to be effective in medical image segmentation. Almost all GAs are semiautomatic, requires either some parameters or domain knowledge such as number of segments, shape, texture etc. Differential Evolution (DE) is a simple and robust evolutionary algorithm and Automatic Clustering using Differential Evolution (ACDE) is a variant of DE. There is no study in medical image segmentation using ACDE. This study is made an attempt to extract the shape of the tissues in medical images automatically using ACDE. The performance of the ACDE algorithm in determining shape of breast cancer, lung tissues has been studied using different ultrasound images. The experimental results are compared with the regions identified by the radiologist and have demonstrated that the ACDE can extract shape of the tissues automatically (without domain knowledge) and helpful in operation surgery and radiology to cure diseases like breast cancer.
PDF Fulltext XML References Citation Report Citation
 RELATED ARTICLES:
  •    Multi-Objective Differential Evolution Algorithm for Solving Engineering Problems
  •    A Modified Tabu Search Approach for the Clustering Problem
  •    A New Differential Evolutionary Algorithm with Neighborhood Search
  •    Segmentation of Satellite Imagery using RBF Neural Network and Genetic Algorithm
How to cite this article:

K. Karteeka Pavan, V. Sesha Srinivas, A. SriKrishna and B. Eswara Reddy, 2012. Automatic Tissue Segmentation in Medical Images using Differential Evolution. Journal of Applied Sciences, 12: 587-592.

DOI: 10.3923/jas.2012.587.592

URL: https://scialert.net/abstract/?doi=jas.2012.587.592
COMMENT ON THIS PAPER
 
 
 

 
  Bullet Navigation
 
  Page Icon Online First
  Page Icon Current Issue
  Page Icon Previous Issues
  Page Icon Editorial Board
  Page Icon Submit a Manuscript
  Page Icon Guide to Authors
  Page Icon Subscribe to E-alerts

  Bullet Indexed In
 
  Page Icon AGRIS
  Page Icon ASCI-Database
  Page Icon Asian Digital Library
  Page Icon Chemical Abstract Services
  Page Icon Google Scholar

Science Alert
Curve Bottom