title: Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound.
abstract: 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.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rio Patras, Greece. tsantis@med.upatras.gr
authors.forename: Stavros
authors.initials: S
authors.lastname: Tsantis
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kalatzis
authors.forename: Nikos
authors.initials: N
authors.lastname: Piliouras
authors.forename: Nikos
authors.initials: N
authors.lastname: Dimitropoulos
authors.forename: George
authors.initials: G
authors.lastname: Nikiforidis
mesh_terms: 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