Biomedical Image Segmentation using Optimized Fuzzy C-mean Algorithm

Abstract

Background/Objectives: Automatic segmentation of brain MRI has an important role in image research along with medical image processing. It has been investigated widely in recent research. It helps for patient diagnosis for different diseases its value concerns in diagnostics through various biomedical images such as PET, CT, MRI and X-ray. In this paper, we analyzed for different biomedical images using partition method. The objective is to detect patch in the biomedical images that may lead to tumors. Methods/Statistical Analysis: The objective of segmentation is to divide the complete image into informative regions and respective specific application. Segmentation separates the image from the background, read the contents and isolating it. Both the concept of clustering by fuzzy technique with edge based segmentation method where standard methods like Sobel, Prewitt edge detectors are applied. Further it is optimized using evolutionary algorithm for efficient minimization of the objective function to improve classification accuracy. Findings: To find the smooth image Gaussian filter is used. Successive segmentation has been performed to detect the patch of desired region. It is observed for different images and compared. Improvements/Applications: It will be helpful for clinical analysis and observe the quality of images for diagnosis of diseases. Keywords: Clustering, Fuzzy C-mean Algorithm, Genetic Algorithm, Optimization, PSO, Thresholding