Systematic construction of texture features for Hashimoto’s lymphocytic thyroiditis recognition from sonographic images

Abstract

The success of discrimination between normal and inflamed parenchyma of thyroid gland by means of automatic texture analysis is largely determined by selecting descriptive yet simple and independent sonographic image features. We replace the standard non-systematic process of feature selection by systematic feature construction based on the search for the separation distances among a clique of n pixels that minimise conditional entropy of class label given all data. The procedure is fairly general and does not require any assumptions about the form of the class probability density function. We show that a network of weak Bayes classifiers using 4-cliques as features and combined by majority vote achieves diagnosis recognition accuracy of 92%, as evaluated on a set of 741 B-mode sonographic images from 39 subjects. The results suggest the possibility to use this method in clinical diagnostic process.