Extraction of breast border and removal of pectoral muscle in wavelet domain

Extraction of the breast border and simultaneously exclusion of pectoral muscle are principal steps for diagnosing of breast cancer based on mammogram data. The objective of propose method is to classify the mediolateral oblique fragment of the pectoral muscle. The extraction of breast region is performed using the multilevel wavelet decomposition of mammogram images. Moreover, artifact suppression and pectoral muscle detection is carried out by morphological operator. The efficient extraction with higher accuracy is validated on the set of 322 digital images taken from MIAS dataset.

[1]  T. E. Taha,et al.  K2. Automatic pectoral muscle boundary detection in mammograms using eigenvectors segmentation , 2012, 2012 29th National Radio Science Conference (NRSC).

[2]  Mislav Grgic,et al.  Breast border extraction and pectoral muscle detection using wavelet decomposition , 2009, IEEE EUROCON 2009.

[3]  S. Sathiya Devi,et al.  Breast Region Extraction and Pectoral Removal by Pixel Constancy Constraint Approach in Mammograms , 2016 .

[4]  Muhammad Nazeer,et al.  Digital image watermarking using partial pivoting lower and upper triangular decomposition into the wavelet domain , 2015, IET Image Process..

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

[6]  Jaime S. Cardoso,et al.  Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[7]  Shohreh Kasaei,et al.  Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..

[8]  Ricardo A. M. Valentim,et al.  Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy , 2016, BioMedical Engineering OnLine.

[9]  Yangkang Chen,et al.  Separation of simultaneous sources using a structural-oriented median filter in the flattened dimension , 2016, Comput. Geosci..

[10]  André Victor Alvarenga,et al.  An Automatic Method for Delineating the Pectoral Muscle in Mammograms , 2007 .

[11]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[12]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[13]  V. K. Govindan,et al.  Computer-Aided Identification of the Pectoral Muscle in Digitized Mammograms , 2010, Journal of Digital Imaging.

[14]  P. Raviraj.  The Modified 2D-Haar Wavelet Transformation in Image Compression , 2007 .

[15]  Rekha Lakshmanan,et al.  Pectoral Muscle Boundary Detection in Mammograms Using Homogeneous Contours , 2015, 2015 Fifth International Conference on Advances in Computing and Communications (ICACC).

[16]  Meinhard Kieser,et al.  A method for using real world data in breast cancer modeling , 2016, J. Biomed. Informatics.

[17]  Andrew C. Singer,et al.  Minimum mean squared error equalization using a priori information , 2002, IEEE Trans. Signal Process..

[18]  Mariusz Bajger,et al.  Two graph theory based methods for identifying the pectoral muscle in mammograms , 2007, Pattern Recognit..

[19]  Xin Yuan,et al.  Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model , 2009, Journal of Zhejiang University SCIENCE C.