Breast Masses Classification using a Sparse Representation

Breast mass detection and classification in mammograms is considered a very difficult task in medical image analysis. In this paper, we present a novel approach for classification of masses in digital mammograms according with their severity (benign or malign). Unlike other approaches, we do not segment masses but instead, we attempt to describe entire regions of interest (RoIs) based on a sparse representation. A set of patches selected by a radiologist in a RoI are characterized by their projection onto learned dictionaries, constructed previously from classified regions. Finally, the region class was identified using a decision rule algorithm. The strategy was assessed in a set of 80 masses with different shapes extracted from the DDSM database. The classification was compared with a ground truth already provided in the data base, showing an average accuracy rate of 70%.

[1]  Rangaraj M. Rangayyan,et al.  Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[3]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[4]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[5]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[6]  Claudia Mello-Thoms,et al.  Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library. , 2007, Academic radiology.

[7]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[8]  Jianhua Xuan,et al.  A preliminary study of content-based mammographic masses retrieval , 2007, SPIE Medical Imaging.

[9]  Fabián Narváez,et al.  Automatic BI-RADS Description of Mammographic Masses , 2010, Digital Mammography / IWDM.

[10]  F. Cheevasuvit,et al.  Contrast enhancement using multipeak histogram equalization with brightness preserving , 1998, IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on Circuits and Systems. Microelectronics and Integrating Systems. Proceedings (Cat. No.98EX242).

[11]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[12]  Robert M. Nishikawa,et al.  Current status and future directions of computer-aided diagnosis in mammography , 2007, Comput. Medical Imaging Graph..

[13]  Brijesh Verma,et al.  A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques , 2001, IEEE Transactions on Information Technology in Biomedicine.

[14]  Tim Byers,et al.  Mammography screening matters for young women with breast carcinoma , 2003, Cancer.

[15]  Bruno A. Olshausen,et al.  Principles of Image Representation in Visual Cortex , 2003 .

[16]  Kjersti Engan,et al.  DETECTION OF MASSES IN MAMMOGRAMS BY WATERSHED SEGMENTATION AND SPARSE REPRESENTATIONS USING LEARNED DICTIONARIES , 2005 .

[17]  David Gur,et al.  Computer-aided detection performance in mammographic examination of masses: assessment. , 2004, Radiology.

[18]  Hae-Kwang Kim,et al.  Region-based shape descriptor invariant to rotation, scale and translation , 2000, Signal Process. Image Commun..