Abstract
Heterogeneous thyroid nodules with distinct components are similar to background in ultrasound image. This results in a difficult task when radiologists and physicians manually delineate the complete shape of a nodule, or distinguish what kind of components it has. Hence, this paper presents an automatic process for nodule segmentation and component classification. A decision tree algorithm is used to segment the possible nodular area. A refinement process is then applied to recover the nodular shape. Finally, a hierarchical classification method based on support vector machine (SVM) is used to identify the components in the nodular lesion. Experimental results of the proposed approach were compared with those of other segmentation methods and showed a good performance.