Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound.

An SVM-based image analysis system was developed for assessing the malignancy risk of thyroid nodules. Ultrasound images of 120 cytology confirmed thyroid nodules (78 low-risk and 42 high-risk of malignancy) were manually segmented by a physician using a custom developed software in C++. From each nodule, 40 textural features were automatically calculated and were used with the SVM algorithm in the design of the image analysis system. Highest classification accuracy was 96.7%, misdiagnosing two high-risk and two low-risk thyroid nodules. The proposed system may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.