Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms

Previous works on breast tissue identification and abnormalities detection notice that the feature extraction process is affected if the region processed is not well focused. Thereby, it is important to split the mammogram into interesting regions to achieve optimal breast parenchyma measurements, breast registration or to put into focus a technique when we search for abnormalities. In this paper, we review most of the relevant work that has been presented from 80's to nowadays. Secondly, an automated technique for segmenting a digital mammogram into breast region and background, with pectoral muscle suppression is presented.

[1]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[2]  R. Chandrasekhar,et al.  Gross segmentation of mammograms using a polynomial model , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  John M. Gauch,et al.  Image segmentation and analysis via multiscale gradient watershed hierarchies , 1999, IEEE Trans. Image Process..

[4]  Georgia D. Tourassi,et al.  Self-organizing maps for masking mammography images , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

[5]  Laurence P. Clarke,et al.  Multiresolution Wavelet Approach for Separating the Breast Region from the Background in High Resolution Digital Mammography , 1998, Digital Mammography / IWDM.

[6]  K Doi,et al.  Automated segmentation of digitized mammograms. , 1995, Academic radiology.

[7]  Berkman Sahiner,et al.  Computerized image analysis: estimation of breast density on mammograms , 2000, Medical Imaging: Image Processing.

[8]  P. G. Tahoces,et al.  Automatic detection of breast border and nipple in digital mammograms. , 1996, Computer methods and programs in biomedicine.

[9]  Olli Nevalainen,et al.  Interactive Segmentation of the Breast Region from Digitized Mammograms with United Snakes , 1999 .

[10]  C.J.S. deSilva,et al.  Skin-air interface extraction from mammograms using an automatic local , 2000 .

[11]  J L Semmlow,et al.  A fully automated system for screening xeromammograms. , 1980, Computers and biomedical research, an international journal.

[12]  Nico Karssemeijer,et al.  Combining Single View Features and Asymmetry for Detection of Mass Lesions , 1998, Digital Mammography / IWDM.

[13]  Dev P. Chakraborty,et al.  Breast tissue density quantification via digitized mammograms , 2001, IEEE Transactions on Medical Imaging.

[14]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[15]  N Ibrahim,et al.  Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database , 1997, Physics in medicine and biology.

[16]  N. Petrick,et al.  Classification of compressed breast shapes for the design of equalization filters in x-ray mammography. , 1998, Medical physics.

[17]  Jorge L. C. Sanz,et al.  Machine Vision Algorithms for Automated Inspection Thin-Film Disk Heads , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  N. Boyd,et al.  Automated analysis of mammographic densities. , 1996, Physics in medicine and biology.

[19]  M L Giger,et al.  Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images. , 1991, Medical physics.

[20]  Y. Attikiouzel,et al.  Automatic pectoral muscle segmentation on mammograms by straight line estimation and cliff detection , 2001, The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001.

[21]  A. Evans.  Breast Imaging Reporting and Data Systems , 1994 .

[22]  Yasuyo Kita,et al.  Three-dimensional reconstruction of microcalcification clusters from two mammographic views , 2001, IEEE Transactions on Medical Imaging.

[23]  Michael A. Wirth,et al.  Segmentation of the breast region in mammograms using snakes , 2004, First Canadian Conference on Computer and Robot Vision, 2004. Proceedings..

[24]  D. Nikitenko,et al.  A fuzzy approach to segmenting the breast region in mammograms , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[25]  T K Lau,et al.  Automated detection of breast tumors using the asymmetry approach. , 1991, Computers and biomedical research, an international journal.

[26]  Rangaraj M. Rangayyan,et al.  Automatic identification of the pectoral muscle in mammograms , 2004, IEEE Transactions on Medical Imaging.