Feature Extraction and Classification of Automatically Segmented Lung Lesion Using Improved Toboggan Algorithm

K. Bavya, Mr. P. Julian

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Feature Extraction and Classification of Automatically Segmented Lung Lesion Using Improved Toboggan Algorithm
( Vol-4,Issue-6,June 2017 )

Author(s):

K. Bavya, Mr. P. Julian

Keywords:

computed tomography (CT), improved toboggan algorithm, local binary pattern(LBP), wavelet, contourlet, grey level co-occurrence matrix(GLCM), support vector machine(SVM), K-nearest neighbour(KNN).

Abstract:

The accurate detection of lung lesions from computed tomography (CT) scans is essential for clinical diagnosis. It provides valuable information for treatment of lung cancer. However, the process is exigent to achieve a fully automatic lesion detection. Here, a novel segmentation algorithm is proposed, it’s an improved toboggan algorithm with a three-step framework, which includes automatic seed point selection, multi-constraints lesion extraction and the lesion refinement. Then, the features like local binary pattern (LBP), wavelet, contourlet, grey level co-occurence matrix (GLCM) are applied to each region of interest of the segmented lung lesion image to extract the texture features such as contrast, homogeneity, energy, entropy and statistical extraction like mean, variance, standard deviation, convolution of modulated and normal frequencies. Finally, support vector machine (SVM) and K-nearest neighbour (KNN) classifiers are applied to classify the abnormal region based on the performance of the extracted features and their performance is been compared. The accuracy of 97.8% is been obtained by using SVM classifier when compared to KNN classifier. This approach does not require any human interaction for lesion detection. Thus, the improved toboggan algorithm can achieve precise lung lesion segmentation in CT images. The features extracted also helps to classify the lesion region of lungs efficiently.

DOI:

10.22161/ijaers.4.6.1

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[1] R. Siegel, D. Naishadham, and A. Jemal, "Cancer statistics, 2013," CA Cancer J Clin, vol. 63, pp. 11-30, Jan 2013.
[2] H. J. W. L. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Cavalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J. Gillies, and P. Lambin, “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.,” Nat. Commun., vol. 5, p. 4006, 2014.
[3] D. M. Campos, A. Simões, I. Ramos, and A. Campilho, “Feature-Based Supervised Lung Nodule Segmentation,” no. Ci, pp. 23–26, 2014.
[4] S. Diciotti, S. Lombardo, M. Falchini, G. Picozzi, and M. Mascalchi, “Automated segmentation refinement of small lung nodules in CT scans by local shape analysis,” IEEE Trans. Biomed. Eng., vol. 58, no. 12 PART 1, pp. 3418–3428, 2011.
[5] J. Song, C. Yang, et al. “A New Quantitative Radiomics Approach for Non-Small Cell Lung Cancer (NSCLC) Prognosis.” presented at the 101nd Int. Conf. Radiological Society of North America, Chicago, Illinois, November 29 - December 04, 2015.
[6] Jiangdian Song, Caiyun Yang, Li Fan, Kun Wang, Feng Yang, Shiyuan Liu* and Jie Tian* “Lung lesion extraction using a toboggan based growing automatic segmentation approach,” IEEE Transactions on Medical Imaging .2015.
[7] S. R. Dubey, S. K. Singh, and R. K. Singh, “A multi-channel based illumination compensation mechanism for brightness invariant image retrieval,” Multimedia Tools and Applications, pp. 1-31, 2014.
[8] A. B. L. Larsen, J. S. Vestergaard, and R. Larsen, “HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling,” IEEE Trans. Med. Imag., vol. 33, no. 7, pp. 1573-1580, 2014
[9] S. Murala and Q. M. J. Wu, “Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval,” IEEE Journal of Biomedical and Health Informatics, vol.18, no.3, pp. 929-938, 2014.
[10] R. Rahmani, S. A. Goldman, H. Zhang, S. R. Cholleti, and J. E. Fritts, “Localized content-based image retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1902–1912, 2008.
[11] K. Konstantinidis, A. Gasteratos, I. Andreadis, “Image retrieval based on fuzzy color histogram processing,” Optics Communications, vol. 248, no. 4–6, pp. 375–386, 2005.
[12] Shiv Ram Dubey, Satish Kumar Singh, and Rajat Kumar Singh,” Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases,” IEEE,2015
[13] D. Wu, L. Lu, J. Bi, Y. Shinagawa, K. Boyer, A. Krishnan, and M. Salganicoff, “Stratified learning of local anatomical context for lung nodules in CT images,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2791–2798.
[14] A. a Farag, H. E. A. El Munim, J. H. Graham, and A. a Farag, “A novel approach for lung nodules segmentation in chest CT using level sets.,” IEEE Trans. Image Process., vol. 22, no. 12, pp. 5202–13, 2013.
[15] A. Mansoor, U. Bagci, Z. Xu, B. Foster, K. N. Olivier, J. M. Elinoff, et al., "A generic approach to pathological lung segmentation," IEEE Trans Med Imaging, vol. 33, pp. 2293-310, Dec 2014
[16] A. S. 3Rd and S. WF, "Automated lung segmentation for thoracic CT impact on computer-aided diagnosis," Academic Radiology, vol. 11, pp. 1011-1021, 2004.
[17] B. Golosio, G. L. Masala, A. Piccioli, P. Oliva, M. Carpinelli, R. Cataldo, P. Cerello, F. De Carlo, F. Falaschi, M. E. Fantacci, G. Gargano, P. Kasae, and M. Torsello, “A novel multithreshold method for nodule detection in lung CT.,” Med. Phys., vol. 36, no. 8, pp. 3607–3618, 2009.
[18] M. Tan, R. Deklerck, B. Jansen, M. Bister, and J. Cornelis, “A novel computer-aided lung nodule detection system for CT images,” Med. Phys., vol. 38, no. 10, p. 5630, 2011.
[19] Tan, W., Yang, J., Bian, Z., Gong, Z., & Zhao, D. (2014). “Automatic extraction of 3d airway tree from multislice computed tomography images.” Journal of Medical Imaging & Health Informatics, volume 4, 768-775(8).
[20] J. Fairfield, “Toboggan contrast enhancement for contrast segmentation,” [1990] Proceedings. 10th Int. Conf. Pattern Recognit., vol. i, pp. 712–716, 1990.
[21] E. N. Mortensen and W. a. Barrett, “Toboggan-based intelligent scissors with a four-parameter edge model,” Proceedings. 1999 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (Cat. No PR00149), vol. 2, pp. 452–458, 1999
[22] M. Nakata, H. Saeki, I. Takata, Y. Segawa, H. Mogami, K. Mandai, and K. Eguchi, “Focal ground-glass opacity detected by low-dose helical CT Chest,” vol. 121, no. 5, pp. 1464–1467, 2002.
[23] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in 0Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., vol. 2. Jun. 2006,pp. 2169–2178.
[24] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognit., vol. 42, no. 3, pp. 425–436, 2009.
[25] J. Mazanec, M. Melisek, M. Oravec, J. Pavlovicova, “Support Vector Machine, PCA and LDA in Face Recognition”, Journal of Electrical Engineering, vol. 59, No. 4, pp. 203-209, 2008.
[26] H. Zhang; Berg, A.C.; Maire, M.; Malik, J., "SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition," Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on , vol.2, no., pp.2126,2136, 2006
[27] Q. Song, W. J. Hu and W. F. Xie, “Robust support vector machine for bullet hole image classification,” IEEE Transactions on Systems, Man and Cybernetics - Part C, vol. 32, no. 4, pp. 440-448, 2002.
[28] L. Zhang, W. Zhou and L. Jiao, “Wavelet support vector machine,” IEEE Transactions on Systems, Man, and Cybernetics - Part B, vol. 34, no. 1, pp. 34-39, 2004.
[29] F. S. Zakeri, H. Behnam, and N. Ahmadinejad, “Classification of benign and malignant breast masses based on shape and texture features in sonography images,” J. Med. Syst., vol. 36, no. 3, pp. 1621-1627, 2012.
[30] Ning Guo, Ruoh-Fang Yen, Georges El Fakhri and Quanzheng Li,” SVM based lung cancer diagnosis using multiple image features in PET/CT,” IEEE,2015
[31] L. Li, Y. Zhang, Y. Zhao, “K-Nearest Neighbors for automated classification of celestial objects”, Science in China Series G-Phys Mech. Astron., vol. 51. No. 7, pp. 916-922, 2008.
[32] S. Murala and Q. M. J. Wu, “Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval,” IEEE Journal of Biomedical and Health Informatics, vol.18, no.3, pp. 929-938, 2014