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.
Tsantis
Stavros
S
Department of Medical Physics, School of Medicine, University of Patras, Rio Patras, Greece. tsantis@med.upatras.gr
Cavouras
Dionisis
D
Kalatzis
Ioannis
I
Piliouras
Nikos
N
Dimitropoulos
Nikos
N
Nikiforidis
George
G
D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D016606:Thyroid Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014463:Ultrasonography