Identification of Thyroid Cancerous Nodule using Local Binary Pattern Variants in Ultrasound Images
 |
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 |
Citation
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. www.ijettjournal.org. published by seventh sense research group
Abstract
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.
Reference
[1] Stavros Tsantis, Nikos Dimitropoulos, Dionisis Cavouras, George Nikiforidis, “Morphological and wavelet features towards sonographic thyroid nodules evaluation”, Computerized Medical Imaging and Graphics, Elsevier, vol. 33, no. 2, pp. 91-99, 2009.
[2] Michalis Savelonas, Dimitris Maroulis, Manolis sangriotis, “A computer aided system for malignancy risk assessment nodules in thyroid US images based on boundary features”, Computer Methods and programs in Biomedicine, Elsevier, vol. 96, no.1, pp. 25-32, 2009.
[3] Grigorescu, S. N., Petkov, N., Kruizinga, P. “Comparison of texture features based on Gabor filters”, IEEE Transactions on Image processing, vol. 11, no. 10, pp. 1160-1167, 2002.
[4] Yuan Y. Tang, Yu Tao, Ernest C.M. Lam, “New method for feature extraction based on fractal behavior”, Pattern Recognition, vol. 35, no. 5, pp. 1071–1081, 2002.
[5] Lorris Nanni, Alessandra Lumini, Sheryl Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis”, Artificial Intelligence in Medicine, Elsevier, vol. 49, no. 2 pp. 117-125, 2010.
[6] Lorris Nanni, Alessandra Lumini, Sheryl Brahurveure descriptors for image classification”, Expert Systems with Applications, Elsevier, vol. 39, no. 3, pp. 3634-3641, 2012.
[7] Sunhua Wan, Xiaolei Huang, Hsiang Chieh Lee, James G Fujimoto, Chao Zhou, “Spoke-LBP and Ring-LBP:New Texture features for tissue classification”, Pattern Recognition (ICPR) 2016 23rd International Conference on, pp. 2440-2445, 2016
[8] Mellisa Cote, Alexandra Branzan Albu, “Robust texture classification by aggregating pixel based LBP statistics”, IEEE Signal Processing Letters, vol 22,no. 11, pp. 614-618, 2015.
[9] Lina Pedraza , Carlos Vargas, Fabian Narvaez , Oscar Duran , Em-ma Munoz, Eduardo Romero, “An open access thyroid ultrasound-image database”, Proceedings of SPIE, Vol. 9287, 2015.
[10] "P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, July 1990.
[11] "G. Grieg, O. Kubler, R. Kikinis, and F. A. Jolesz, “Nonlinear Anisotropic Filtering of MRI Data”, IEEE Transactions on Medical Imaging, vol. 11, no.2, pp. 221-232, June 1992.
[12] Ojala T Pietikainen M, Maeenpaa T, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 24, no. 7, pp. 971-987, 2002.
[13] Y. Guo, G. Zhao, and M. Pietikainen. “Texture Classification using a ¨ Linear Configuration Model based
Descriptor” in 22nd British Mission Vision Conference , pp. 1-10, Sep. 2011.
[14] Zhenhua Guo, Lei Zhang, David Jhang, “A completed modeling of local binary pattern operator for texture classification”, IEEE Transactions on Image Processing, vol. 19, no.6, pp. 1657-1663, June 2010.
[15] Corinna Cortes, Vladimir Vapnik. “Support-Vector Networks”, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[16] T.Saravanan, “Noise Removal In Ultrasound Images”, International Journal of Engineering Trends and Technology (IJETT), Vol 3, Issue 2, No. 4, April 2012.
[17] Suman Pandey, Deepak Kumar Gour, Vivek Sharma, “Comparative Study on Classification of Thyroid Diseases”, International Journal of Engineering Trends and Technology (IJETT), Vol 28, No. 9, October 2015.
[18] Anjali Kapoor, Taranjeet Singh, “Speckle Reducing Filtering for Ultrasound Images”, International Journal of Engineering Trends and Technology (IJETT), Vol 37, No. 5, July 2016
Keywords
Completed local binary pattern, Local binary pattern, Local configuration pattern, Thyroid nodule.