A Novel Approach towards Segmentation of Breast Tumors from Screening Mammograms for Efficient Decision Support System

This paper presents a novel approach to finding a priori interesting regions in mammograms. In order to delineate those regions of interest (ROI-s) in mammograms, which appear to be prominent, a topographic representation called the iso-level contour map consisting of iso-level contours at multiple intensity levels and region segmentation based-thresholding have been proposed. The simulation results indicate that the computed boundary gives the detection rate of 99.5% accuracy.

Authors:

• M.Suganthi
• M.Madheswaran

Keywords:

• Breast Cancer
• Mammogram
• and Segmentation.

References:

[1] B.Sahiner,N.Petrick,H.P.Chan,L.M.Hadjiishi,C.Paramagul,M.A.Helvie,
M.N.Gurcan"Computer-aided characterization of mammographic
masses: accuracy of mass segmentation and its effects on
characterization," IEEE Tran.Med.Imah.,Vol 20,pp.1275-
1284,Dec.2001.
[2] T. Tweed and S. Miguet. Automatic detection of regions of interest in
mammographies based on a combined analysis of texture and
histogram", IEEE Computer Society, volume 02, pages 448-452, ,
2002..
[3] Armando Bazzani , Alessandro Bevilacqua, Dante Bollini Rosa
Brancaccio and Gianluca Zamboni "Automatic detection of clustered
microcalcifications in digital mammograms using an SVM classifier"
European Symposium on Artificial Neural Networks Brugs, ,pp .195-
200 April 2000 .
[4] Bick U, Giger ML, Schmidt RA, Nishikawa RM, Wolverton DE, Doi
K."Automated segmentation of digitized mammograms" Academic
Radiology Vol 2 pp.1-9. 1995
[5] Abdel-Mottaleb M, Carman CS, Hill CR, Vafai S. In: Doi K, Giger ML,
"Locating the boundary between the breast skin edge and the
background in digitized mammograms Elsevier, . pp. 467-70, 1996.
[6] Meandez AJ, Tahoces PG, Lado MJ, Souto M, Correa JL, Vidal
JJ."Automatic detection of breast border and nipple in digital
mammograms"Comput Meth Prog Biomed Vol 49,pp 253-62,1996.
[7] Rezaee, M.R., van der Zwet, P.M.J., Lelieveldt, B.P.F.,van der Geest,
R.J. & Reiber, J.H.C. "A multiresolution image segmentation technique
based on pyramidal segmentation and fuzzy clustering" IEEE
Transactions on Image Processing, Vol 9, pp. 1238-1248, 2000.
[8] Strickland, R.N. & Hahn, H.I. "Wavelet transforms for detecting
microcalcifications in mammograms". IEEE Transactions on Medical
Imaging Vol 15,pp 218-229,1996.
[9] Karssemeijer N. "Adaptive noise equalization and recognition of
microcalcification clusters in mammograms" Int J Pattern Recog.
Artificial Intell Vol 7,pp.1357-76, 1993.
[10] L. Alvarez, L. Mazorra, Signal and image restoration using shock filters
and anisotropic diffusion, SIAM Journal on Numerical Analysis
31(2),pp. 590-605,1994
[11] R. J. Ferrari, A.F. Frere , R.M. Rangayyan ,J.E.L. Desautels ,R. A.
Borges ,"Identification of the breast boundary in mammograms using
active contour models"Med. Biol. Eng. Comput. Vol 42, pp.201-208,
2004.
[12] Davies DH, Dance DR. "Automatic computer detection of clustered
calcifications in digital mammograms" Phys Med Biol Vol 35:1111-8,
1990