A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter

Minute calcium deposits that appear as bright spots on mammograms are known as microcalcifications. Calcifications in ducts and lobules are common markers of malignancy; therefore its detection plays a vital role in early detection of breast cancer. This paper proposes a novel framework for edge detection of microcalcifications using a combination of non-linear enhancement operator and morphological filter. The contrast improvement of the region of interest for better visualization of mammographic abnormalities is obtained using the proposed non-linear operator. This enhancement operator is versatile in approach, producing promising results for dense as well as non dense breast tissues. The enhanced ROI is then convolved with the proposed mask of two stage iterative discrete morphological gradient operator with variable sized structuring elements leading to improved edge detection of microcalcifications.

[1]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[2]  Petros Maragos,et al.  Differential morphology and image processing , 1996, IEEE Trans. Image Process..

[3]  John R. Benson,et al.  Early Breast Cancer: From Screening to Multidisciplinary Management , 1998 .

[4]  Felice Andrea Pellegrino,et al.  Edge detection revisited , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  L. Costaridou,et al.  A wavelet-based spatially adaptive method for mammographic contrast enhancement , 2003, Physics in medicine and biology.

[6]  Isaac N. Bankman,et al.  Handbook of Medical Imaging. Processing and Analysis , 2002 .

[7]  Dimitrios I. Fotiadis,et al.  Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques , 2008, Comput. Biol. Medicine.

[8]  D. Andina,et al.  Feature extraction using coordinate logic filters and Artificial Neural Networks , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[9]  C.S. Lindquist,et al.  New edge detection algorithms based on adaptive estimation filters , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[10]  Andrew F. Laine,et al.  Wavelets for contrast enhancement of digital mammography , 1995 .

[11]  Andrew D. A. Maidment,et al.  Image processing algorithms for digital mammography: a pictorial essay. , 2000, Radiographics : a review publication of the Radiological Society of North America, Inc.

[12]  Yitzhak Yitzhaky,et al.  A Method for Objective Edge Detection Evaluation and Detector Parameter Selection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Heng-Da Cheng,et al.  A novel approach to microcalcification detection using fuzzy logic technique , 1998, IEEE Transactions on Medical Imaging.

[14]  A. Fenster Handbook of Medical Imaging, Processing and Analysis , 2001 .

[15]  M. Giger,et al.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. , 2008, Medical physics.

[16]  Zhe Wu,et al.  Digital mammography image enhancement using improved unsharp masking approach , 2010, 2010 3rd International Congress on Image and Signal Processing.

[17]  Mahmood Fathy,et al.  A classified and comparative study of edge detection algorithms , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[18]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[19]  J. Salvado,et al.  Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement , 2005, IEEE International Workshop on Intelligent Signal Processing, 2005..

[20]  James H. Elder,et al.  Are Edges Incomplete? , 1999, International Journal of Computer Vision.

[21]  Jian Fan,et al.  Mammographic feature enhancement by multiscale analysis , 1994, IEEE Trans. Medical Imaging.

[22]  B. Reljin,et al.  Local contrast enhancement in digital mammography by using mathematical morphology , 2005, International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005..

[23]  Robin N. Strickland,et al.  Wavelet transforms for detecting microcalcifications in mammograms , 1996, IEEE Trans. Medical Imaging.

[24]  Isaac N. Bankman,et al.  Handbook of medical imaging , 2000 .

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

[26]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[27]  Joel Quintanilla-Domínguez,et al.  Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[28]  Branimir Reljin,et al.  Enhancement of microcalcifications in digitized mammograms: Multifractal and mathematical morphology approach , 2010 .

[29]  Jie Gao,et al.  Microcalcification detection using combination of wavelet transform and morphology , 2006, 2006 8th international Conference on Signal Processing.

[30]  Basil G. Mertzios,et al.  Applications of coordinate logic filters in image analysis and pattern recognition , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..