Identification of Thyroid Cancerous Nodule using Local Binary Pattern Variants in Ultrasound Images

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-49 Number-6
Year of Publication : 2017
Authors : Nanda S, M Sukumar
DOI :  10.14445/22315381/IJETT-V49P256


Nanda S, M Sukumar "Identification of Thyroid Cancerous Nodule using Local Binary Pattern Variants in Ultrasound Images", International Journal of Engineering Trends and Technology (IJETT), V49(6),369-374 July 2017. ISSN:2231-5381. published by seventh sense research group

Most of the thyroid nodules are heterogeneous in nature with dissimilar echo patterns. Hence texture characterization plays a major role in discriminating benign and malignant nodules in thyroid ultrasound images. This paper addresses the classification of thyroid nodule through local binary pattern (LBP), local configuration pattern (LCP) and completed local binary pattern (CLBP) variants. This work comprises of 60 thyroid ultrasound images. LBP, LCP and CLBP features are extracted from the thyroid images. These features are used to train and test support vector machine (SVM). Accuracy, sensitivity, specificity, positive predictive value and negative predictive values are calculated. Performances of the classifier with linear, polynomial and radial basis function (RBF) kernels are compared. Best accuracy of 94.5% has been achieved when CLBP features are given to SVM of different forms.

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Completed local binary pattern, Local binary pattern, Local configuration pattern, Thyroid nodule.