Author : Shruthi C V 1
Date of Publication :7th May 2016
Abstract: Thyroid is a butterfly-shaped gland in the front of the neck. It is found below the voice box. Thyroid nodule is one of the indicative of thyroid cancer. Nodule can be due to the growth of thyroid cells or a cyst in the thyroid gland. It is very important to differentiate between the thyroid nodule as benign or malignant. This paper presents characterization and classification of thyroid nodule using Ultrasonography. It includes extraction of set of features by using Gray Level Co-occurrence Matrix GLCM, Wavelet Transform and Local Ternary Pattern (LTP). These features are reduced to set of selected features by using PCA algorithm. The selected features are given to SVM classifier for the classification of thyroid nodule as benign or malignant. The performance of classifiers is evaluated with the accuracy, sensitivity and specificity.
Reference :
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[1] Priti Shivaji Dhaygude and S. M. Handore “Feature Extraction of Thyroid Nodule US images using GLCM”, International Journal of Science and Research, Volume 5 Issue 1 January 2016.
[2] Ms. Nikita Singh and Mrs Alka Jindal, “A segmentation method and classification of diagnosis for thyroid nodules”, Journal of Computer Engineering, Volume 1, Issue 6, July-Aug 2012
[3] Shrikant D.Kale and Krushil M. Punwatkar, “Texture Analysis of Ultrasound Medical Images for Diagnosis of Thyroid Nodule using Support Vector Machine”, International Journal of Computer Science and Mobile Computing, Volume 2, Issue 10, October 2013.
[4] Robert M. Haralick, K. Shanmugam and Its’hak Dinstein, “Textural Features for Image Classification”, IEEE Transactions on systems, Man and Cybernetics, Vol. SMC-3, No.6, November 1973.
[5] Ramaraj. M and Dr. S. Raghavan, “A survey of Wavelet Techniques and Multi-resolution Analysis for Cancer Diagnosis”, International Conference on Computer, communication & Electrical Technology, March 2011.
[6] Vikas and Amanpreet Kaur, “Face Recognition using Local Ternary Pattern for Low Resolution Image”, International Journal of Science and Research, Volume 3 Issue 12, December 2014.
[7] T. Ojala, M. Pietikainen and D. Harwood, “A Comparative study of Texture Measures with Classification based on Feature Distributions”, Pattern Recognition, Volume 29, 1996.
[8] Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions”, IEEE Transactions on Image Processing, Volume 19, Issue 6, 2010.