ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Elm Based Cad System to Classify Mammograms by the Combination of CLBP and Contourlet


Affiliations
1 Department of Computer Science Engineering, Panimalar Institute of Technology, India
2 Sri Krishna College of Engineering and Technology, India
     

   Subscribe/Renew Journal


Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP) and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM) is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.

Keywords

Breast Cancer, Microcalcification, Mammogram, Classification.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Over 17 Lakh New Cancer Cases in India by 2020: ICMR, Available at: http://icmr.nic.in/icmrsql/archive/2016/7.pdf.

  • S.M. Astley and F.J. Gilbert, “Computer-Aided Detection in Mammography”, Clinical Radiology, Vol. 59, No. 5, pp. 390-399, 2004.

  • Daniel C. Moura and Miguel A. Guevara Lopez, “An Evaluation of Image Descriptors combined with Clinical Data for Breast Cancer Diagnosis”, International Journal of Computer Assisted Radiology and Surgery, Vol. 8, No. 4, pp. 561-574, 2013.

  • Suman Shrestha, “Image Denoising using New Adaptive based Median Filter”, Signal and Image Processing : An International Journal, Vol. 5, No. 4, pp. 1-13, 2014.

  • Yuhui Zheng, Byeungwoo Jeon, Danhua Xu, Q.M. Jonathan Wu and Hui Zhang, “Image Segmentation by Generalized Hierarchical Fuzzy C-Means Algorithm”, Journal of Intelligent and Fuzzy Systems, Vol. 28, No. 2, pp. 961-973, 2015.

  • M. Mirmehdi, X. Xie and J. Suri, “Handbook of Texture Analysis”, Imperial College Press, 2009.

  • Mohamed Meselhy Eltoukhy, Ibrahima Faye and Brahim Belhaouari Samir, “A Statistical based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation”, Computers in Biology and Medicine, Vol. 42, No. 1, pp. 123-128, 2012.

  • I. Christoyianni, A. Koutras, E. Dermatas and G. Kokkinakis, “Computer Aided Diagnosis of Breast Cancer in Digitized Mammograms”, Computerized Medical Imaging and Graphics, Vol. 26, No. 5, pp. 309-319, 2002.

  • A. Karahaliou, S. Skiadopoulos, I. Boniatis, P. Sakellaropoulos, E. Likaki, G. Panayiotakis and L Costaridou, “Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis”, The British Journal of Radiology, Vol. 80, No. 956, pp. 648-656, 2007.

  • S.J.S. Gardezi, I. Faye and M.M. Eltoukhy, “Analysis of Mammogram Images based on Texture Features of Curvelet Sub-Bands”, Proceedings of 5th International Conference on Graphic and Image Processing, pp. 906924-906926, 2014.

  • A. Oliver, X. Llado, R.Marti, J. Freixenet and R. Zwiggelaar, “Classifying Mammograms using Texture Information”, Proceedings of International Conference on Medical Image Understanding and Analysis, pp. 223-227, 2007.

  • A. Oliver, X. Llad, J. Freixenet and J. Mart, “False Positive Reduction in Mammographic Mass Detection using Local Binary Patterns”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 286-293, 2007.

  • Sophie Paquerault, Nicholas Petrick, Heang-Ping Chan, Berkman Sahiner and Mark A. Helvie, “Improvement of Computerized Mass Detection on Mammograms: Fusion of Two-View Information”, Medical Physics, Vol. 29, No. 2, pp. 238-247, 2002.

  • Y.A.S. Duarte, M.Z. Nascimento and D.L.L. Oliveira, “Classification of Mammographic Lesion based in Completed Local Binary Pattern and using Multiresolution Representation”, Journal of Physics: Conference Series, Vol. 490, pp. 1-5, 2013.

  • M.M. Eltoukhy, I. Faye, and B.B. Samir, “Curvelet based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram”, Proceedings of International Conference on Intelligent and Advanced Systems, pp. 1-5, 2010.

  • Jae Young Choi and Yong Man Ro, “Multiresolution Local Binary Pattern Texture Analysis combined with Variable Selection for Application to False-Positive Reduction in Computer-Aided Detection of Breast Masses on Mammograms”, Physics in Medicine and Biology, Vol. 57, No. 21, pp. 7029-7034, 2012.

  • S. Chen and D. Zhang, “Robust Image Segmentation using FCM with Spatial Constraints based on New Kernel-Induced Distance Measure”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 34, No. 4, pp. 1907-1916, 2004.

  • D. Pham and J.L. Prince, “An Adaptive Fuzzy C-Means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities”, Pattern Recognition Letters, Vol. 20, No. 1, pp. 57-68, 1999.

  • A. Pedrycz and M. Reformat, “Hierarchical FCM in a Stepwise Discovery of Structure in Data”, Soft Computing, Vol. 10, No. 3, pp. 244-256, 2006.

  • Nicolaos B. Karayiannis, “Generalized Fuzzy C-Means Algorithms”, Proceedings of the 5th IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1036-1042, 1996.

  • Zhenhua Guo, Lei Zhang and David Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”, IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1657-1663, 2010.

  • Minh N. Do and Martin Vetterli, “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106, 2005.

  • Guang-Bin Huang, Hongming Zhou, Xiaojian Ding and Rui Zhang, “Extreme Learning Machine for Regression and Multiclass Classification”, IEEE Transactions on Systems, Man and Cybernetics-Part B, Vol. 42, No. 2, pp. 513-529, 2012.

  • The new MIAS Database. Available: http://www.wiau.man.ac.uk/services/MIAS/MIASfaq.html.

  • Erik Cambria and Guang-Bin Huang, “Extreme Learning Machines”, IEEE Intelligent Systems, pp. 30-59, 2013.

  • L. Wei, Y. Yang, R.M. Nishikawa, M.N. Wernick and A. Edwards, “Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications”, IEEE Transactions on Medical Imaging, Vol. 24, No. 10, pp. 1278-1285, 2005.


Abstract Views: 284

PDF Views: 7




  • Elm Based Cad System to Classify Mammograms by the Combination of CLBP and Contourlet

Abstract Views: 284  |  PDF Views: 7

Authors

S. Venkatalakshmi
Department of Computer Science Engineering, Panimalar Institute of Technology, India
J. Janet
Sri Krishna College of Engineering and Technology, India

Abstract


Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP) and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM) is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.

Keywords


Breast Cancer, Microcalcification, Mammogram, Classification.

References