Classification of thyroid ultrasound images based on shape features analysis

Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying thyroid nodule based on shape feature analysis into two classes, i.e. round to oval and irregular classes. The proposed scheme is tested on 165 thyroid ultrasound images consisting of 61 round to oval images and 104 irregular images. The process is started by filtering image as the preprocessing step followed by segmentation process using active contour and morphological operation. Shape analysis is performed by extracting seven geometric features and 14 moment features. These features are then selected by using correlation based feature selection (CFS). Three selected features are classified using linear support vector machine (SVM). The classification results achieved the level of accuracy, sensitivity, specificity, PPV and NPV at 91.52%, 91.80%, 91.35%, 86.15%, and 95%, respectively. These results indicate that the proposed scheme successfully classified the shape of thyroid nodule.