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

Stavros Tsantis; Dionisis Cavouras; Ioannis Kalatzis; Nikos Piliouras; Nikos Dimitropoulos; George Nikiforidis

doi:10.1016/j.ultrasmedbio.2005.07.009

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

Stavros Tsantis·Dionisis Cavouras·Ioannis Kalatzis·Nikos Piliouras·Nikos Dimitropoulos·George Nikiforidis

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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.

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Algorithms Humans Image Interpretation, Computer-Assisted Risk Assessment Sensitivity and Specificity Thyroid Neoplasms Thyroid Nodule Ultrasonography Journal Article

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Stavros Tsantis, Dionisis Cavouras, Ioannis Kalatzis, Nikos Piliouras, Nikos Dimitropoulos, George Nikiforidis. 2005. Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound.. https://doi.org/10.1016/j.ultrasmedbio.2005.07.009

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