Genetic Algorithm-Based Anisotropic Diffusion Filter and Clustering Algorithms for Thyroid Tumor Detection

Abstract

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to study resemblance, dissimilarity, distance between two sets of images, and effect of ADF on enhancing the CT images. The results showed that ADF increases the segmentation accuracy, where the value of Jaccard index of similarity between the ground truth image and segmented image was increased for all segmentation algorithms, in particular for FCM  and GAFCM where  similarity percent was up to 88%.