Research Article 
Automatic Tissue Segmentation in Medical Images using Differential Evolution
K. Karteeka Pavan, V. Sesha Srinivas, A. SriKrishna and B. Eswara Reddy
Journal of Applied Sciences, 2012, 12(6), 587-592.
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.
ASCI-ID: 35-4446
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