Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images
Document Type
Article
Publication Date
1-1-2016
Abstract
Thyroid cancer commences from an atypical growth of thyroid tissue at the edge of the thyroid gland. Initially, it forms a lump in the throat and an over-growth of this tissue leads to the formation of benign or malignant thyroid nodules. Blood test and biopsies are the standard techniques used to diagnose the presence of thyroid nodules. But imaging modalities can improve the diagnosis and are marked as cost-effective, non-invasive and risk-free to identify the stages of thyroid cancer. This study proposes a novel automated system for classification of benign and malignant thyroid nodules. Raw images of thyroid nodules recorded using high resolution ultrasound (HRUS) are subjected to Gabor transform. Various entropy features are extracted from these transformed images and these features are reduced by locality sensitive discriminant analysis (LSDA) and ranked by Relief-F method. Over-sampling strategies with Wilcoxon signed-rank, Friedmans and Iman-Davenport post hoc tests are used to balance the classification data and also to improve the classification performance. Classifiers such as support vector machine (SVM), k-nearest neighbour (kNN), multi-layered perceptron (MLP) and decision tree are used for the characterization of benign and malignant thyroid nodules. We have obtained a classification accuracy of 94.3% with C4.5 decision tree classifier using 242 thyroid HRUS images. Our developed system can be used to screen the thyroid automatically and assist the radiologists.
Keywords
Thyroid nodules, High resolution, SMOTE, Over-sampling strategies, Post hoc test, Relief-F, LSDA
Divisions
fac_eng,fac_med
Publication Title
Knowledge-Based Systems
Volume
107
Publisher
Elsevier