Classification of benign and malignant masses using bandelet and orthogonal ripplet type II transforms

Breast cancer is one of the leading cause of death among women worldwide. Many CAD systems have been proposed for the early detection of cancer causing masses. In this paper, classification of mammograms from digital database for screening mammography (DDSM) is done using bandelet and orthogonal ripplet type II transforms. In this study, two subsets of mammograms are used from the DDSM database. The first subset contains 360 regions of interest (ROI) of mammograms obtained from howtek scanner and the second subset contains 300 ROIs of mammograms obtained from both lumisys and howtek scanner. Bandelet and orthogonal ripplet type II transform coefficients are extracted for these two subsets. First-order texture features are calculated for the ROIs using the coefficients of bandelet and orthogonal ripplet type II transform. Based on the first-order texture features, the ROIs are classified. The area under the curve for orthogonal ripplet type II transform is and bandelet transform is obtained using 360 mammo...

[1]  Salim Lahmiri,et al.  Hybrid cosine and Radon transform-based processing for digital mammogram feature extraction and classification with SVM , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Hamid Soltanian-Zadeh,et al.  Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms , 2004, Pattern Recognit..

[3]  A. Kandaswamy,et al.  A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine , 2012, Comput. Biol. Medicine.

[4]  Rangaraj M. Rangayyan,et al.  Contour-independent detection and classification of mammographic lesions , 2016, Biomed. Signal Process. Control..

[5]  Stéphane Mallat,et al.  Surface compression with geometric bandelets , 2005, ACM Trans. Graph..

[6]  Nebi Gedik,et al.  A new feature extraction method based on multi-resolution representations of mammograms , 2016, Appl. Soft Comput..

[7]  Sahibsingh A. Dudani The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Jun Xu,et al.  Ripplet transform for feature extraction , 2008, SPIE Defense + Commercial Sensing.

[9]  Zohreh Azimifar,et al.  Contourlet-based mammography mass classification using the SVM family , 2010, Comput. Biol. Medicine.

[10]  Murk J. Bottema,et al.  Constructing and applying higher order textons: Estimating breast cancer risk , 2014, Pattern Recognit..

[11]  C. Floyd,et al.  Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. , 2006, Medical physics.

[12]  Wellington Pinheiro dos Santos,et al.  Detection and classification of masses in mammographic images in a multi-kernel approach , 2016, Comput. Methods Programs Biomed..

[13]  Salim Lahmiri,et al.  DWT and RT-based approach for feature extraction and classification of mammograms with SVM , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[14]  Samir Brahim Belhaouari,et al.  A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram , 2010, Comput. Biol. Medicine.

[15]  Salim Lahmiri,et al.  Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction , 2011, 2011 IEEE 9th International New Circuits and systems conference.

[16]  Stéphane Mallat,et al.  Sparse geometric image representations with bandelets , 2005, IEEE Transactions on Image Processing.

[17]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[18]  Xavier Lladó,et al.  False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns , 2007, MICCAI.

[19]  Banshidhar Majhi,et al.  Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer , 2015, Neurocomputing.

[20]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[21]  Defeng Wang,et al.  Automatic detection of breast cancers in mammograms using structured support vector machines , 2009, Neurocomputing.

[22]  Alessandro Santana Martins,et al.  Classification of masses in mammographic image using wavelet domain features and polynomial classifier , 2013, Expert Syst. Appl..

[23]  A. Kandaswamy,et al.  Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms , 2007, Comput. Medical Imaging Graph..

[24]  Marcelo Zanchetta do Nascimento,et al.  Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm , 2014, Comput. Methods Programs Biomed..

[25]  Ezzeddine Zagrouba,et al.  Breast cancer diagnosis in digitized mammograms using curvelet moments , 2015, Comput. Biol. Medicine.

[26]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[27]  Shyr-Shen Yu,et al.  Automatic detection of abnormal mammograms in mammographic images , 2015, Expert Syst. Appl..