Genetic Algorithms for Thyroid Gland Ultrasound Image Feature Reduction

Abstract

The problem of automatic classification of ultrasound images is addressed. For texture analysis of ultrasound images quantifiable indexes, called features, are used. Classification was performed using Gaussian mixture model based on Bayes classifier. The common problem of texture analysis is a feature selection for classification tasks. In this work we use genetic algorithms for a feature subset selection. Total number of 387 features was used, consisting of spatial an co-occurance statistical texture features (proposed by Muzzolini and Haralick). The classification infers between healthy thyroid gland and thyroid gland with chronic inflammation.

Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

“…Features based on gray level correlation matrix (GLCM) [6], have been used for the detection of cardiac images [1], and the placenta [9]. In another work, GLCM has been utilized to guide the evolution of active contour for detecting the Thyroid Gland [15]. Recently the wavelet approach has been used with support vector machine (SVM) classifier to detect the prostate region [20], and also thresholding based wavelet has been used to remove the noise to detect the prostate [8].…”