Curvelet based feature extraction method for breast cancer diagnosis in digital mammogram

This paper proposes a method for breast cancer diagnosis in digital mammogram. The article focuses on using texture analysis based on curvelet transform for the classification of tissues. The most discriminative texture features of regions of interest are extracted. Then, a nearest neighbor classifier based on Euclidian distance is constructed. The obtained results calculated using 5-fold cross validation. The approach consists of two steps, detecting the abnormalities and then classifies the abnormalities into benign and malignant tumors.

[1]  Essam A. Rashed,et al.  Multiresolution mammogram analysis in multilevel decomposition , 2007, Pattern Recognit. Lett..

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

[3]  David L. Donoho,et al.  Curvelets, multiresolution representation, and scaling laws , 2000, SPIE Optics + Photonics.

[4]  Ibrahim M. Eldokany,et al.  CURVELET FUSION OF MR AND CT IMAGES , 2008 .

[5]  Lucia Dettori,et al.  A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography , 2007, Comput. Biol. Medicine.

[6]  Nguyen Thanh Binh,et al.  OBJECT DETECTION OF SPECKLE IMAGE BASE ON CURVELET TRANSFORM , 2007 .

[7]  Sheng Liu,et al.  Multiresolution detection of spiculated lesions in digital mammograms , 2001, IEEE Trans. Image Process..

[8]  Rafayah Mousa,et al.  Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural , 2005, Expert Syst. Appl..

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Samir Brahim Belhaouari,et al.  Breast cancer diagnosis in digital mammogram using multiscale curvelet transform , 2010, Comput. Medical Imaging Graph..

[11]  Ibrahima Faye,et al.  Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[12]  K. P. Soman,et al.  Insight into Wavelets: From Theory to Practice , 2005 .

[13]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[14]  Zohreh Azimifar,et al.  Contourlet-Based Mammography Mass Classification , 2007, ICIAR.

[15]  Fionn Murtagh,et al.  Wavelet and curvelet moments for image classification: Application to aggregate mixture grading , 2008, Pattern Recognit. Lett..

[16]  Pierre Moulin,et al.  Complexity-regularized image denoising , 2001, IEEE Trans. Image Process..

[17]  Díbio Leandro Borges,et al.  Analysis of mammogram classification using a wavelet transform decomposition , 2003, Pattern Recognit. Lett..