Abstract
In this paper, an algorithm for thyroid nodule assessment in medical ultrasound images is proposed. Ultrasound imaging is a noninvasive diagnostic tool to detect abnormalities of thyroid gland. But the presence of speckle noise limits the contrast resolution and makes diagnosis more difficult. Hence despeckling is used as a pre-processing step. The nodule region is segmented using Fuzzy C-Means clustering. Gray Level Co-occurrence Matrix (GLCM) features are extracted and are utilized for classification using Support Vector Machine (SVM). The performance evaluation of the SVM classifier is done using accuracy, sensitivity and specificity.
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Vanithamani, R., Dhivya, R. (2018). Thyroid Nodule Classification in Medical Ultrasound Images. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_50
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DOI: https://doi.org/10.1007/978-3-319-60618-7_50
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