Breast Skin-Line Estimation and Breast Segmentation in Mammograms using Fast-Marching Method
Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
[1] Kass M, Witkin A, Terzopoulos D., Snakes: Active Contour Models.,
Int. J. Computer Vision, 1987; 1(4), pp 321-31.
[2] Mclnemey T, Terzopoulos D., Deformable models in medical image
analysis: a survey, Med. Img. Anal, 1996; 1(2), pp 91-108.
[3] R. Malladi, J. A. Sethian, and B.C. Vemuri, Shape modeling with front
propagation: A level set approach, in IEEE Trns. On Pattern Analysis
and Machine Intelligence, 1995; 17(2), pp 158-175.
[4] V. Caselles, R. Kimmel, and G. Sapiro, Geodisk Snakes, Proc. of ICCV,
MIT Cambridge MA, 1995.
[5] J.A. Sethian, Level set methods and fast marching method: evolving
interfaces in computational geometry, fluid mechanics, computer vision,
and material science., Cambridge University Press, 1999.
[6] P.C. Johns, M.J. Yaffe, X-ray characterization of normal and neoplastic
breast tissue, Phys. Med. Biol, 1987; 32, pp 675-695.
[7] The Mammographic Image Analysis Society, Digital Mammography
Database ver 1.2,
(http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html).
[8] Wirth W., Nikitenko D., Lyon J., Segmentation of Breast Region in
Mammograms using a Rule-Based Fuzzy Reasoning Algorithm. ICGSTGraphics,
Vision and Image Processing Journal, 2005; 5(2); pp 45-54.
[9] Luc Vincent. Morphological Area Opening and Closing for Grayscale
Images, Proc. NATO Shape in Picture Workshop, Driebergen, The
Netherlands, Springer-Verlag, pp. 197-208, September 1992.
[10] Soille P., Morphological Image Analysis: Principles and Applications.
Springer-Verlag. 2003.
[11] Wirth M.A., Lyon J., Nikitenko N., Stapinski A. Removing Radioopaque
Artifacts from Mammograms using Area Morphology. In Proc. of SPIE
Medical Imaging: Image Processing. pp 1054-1065. 2004.
[12] Salembier P., Serra J. Flat Zones Filtering, Connected Operators, and
Filters by Reconstruction. IEEE Transaction on Image Processing, 4(8),
pp 1153-1160. 1995.
[13] Hoyer A., Spiesberg W., Computerized mammogram processing. In:
Phillips Technical Review. Vol. (38), pp 347-355, 1979.
[14] Lau T., Bischoff W., Automated Detection of Breast Tumours Using the
Asymmetry Approach. In. Computers and Bio-Medical Research. Vol.
(24), pp 273-295, 1991.
[15] Bick U., Giger M. L., Schmidt R.A., Nishikawa R.M., Wolverton D.E.,
Doi K., Automated Segmentation of Digitized Mammograms. Academic
Rediology, Vol. 2(2), pp 1-9, 1995.
[16] Masek M., Attikiouzel Y., Skin-Air Interface Extraction from
Mammograms Using an Automatic Local Thresholding Algorithm. In;
ICB Brono CR, pp 204-206, 2000.
[17] Abdel-Mottaleb M., Carman C.S., Hill C.R., Vafai S., Locating the
Boundary Between the Breast Skin Edge and the Background in
Digitized Mammograms. In Porc. Of the 3rd International Workshop on
Digital Mammography, pp 467-470, 1996.
[18] Mendez A.J., Tahoces P.J., Lado M.J., Souto M., Correa J.L., Vidal J.J.,
Automatic Detection of Breast Boarder and Nipple in Mammograms.
Computer Methods and Programs in Bio-Medicine, 49, pp 253-262,
1996.
[19] Karssemeijer N., te Brake G., Combining Single View Features and
Asymmetry for Detection of Mass Lesions. In IWDM, pp 1107-1108,
1998.
[20] Chandrasekhar R., Attikiouzel Y., Automatic Breast Boarder
Segmentation by Background Modelling and Subtraction. In Proc. of the
5th International Workshop on Digital Mammography, pp 560-565, 2000.
[21] Wirth M.A., Stapinski A., Segmentation of the Breast Region in
Mammograms using Active Contours. In Proc. of Visual Communication
and Image Processing, 5510, pp 1995-2006, 2003.
[1] Kass M, Witkin A, Terzopoulos D., Snakes: Active Contour Models.,
Int. J. Computer Vision, 1987; 1(4), pp 321-31.
[2] Mclnemey T, Terzopoulos D., Deformable models in medical image
analysis: a survey, Med. Img. Anal, 1996; 1(2), pp 91-108.
[3] R. Malladi, J. A. Sethian, and B.C. Vemuri, Shape modeling with front
propagation: A level set approach, in IEEE Trns. On Pattern Analysis
and Machine Intelligence, 1995; 17(2), pp 158-175.
[4] V. Caselles, R. Kimmel, and G. Sapiro, Geodisk Snakes, Proc. of ICCV,
MIT Cambridge MA, 1995.
[5] J.A. Sethian, Level set methods and fast marching method: evolving
interfaces in computational geometry, fluid mechanics, computer vision,
and material science., Cambridge University Press, 1999.
[6] P.C. Johns, M.J. Yaffe, X-ray characterization of normal and neoplastic
breast tissue, Phys. Med. Biol, 1987; 32, pp 675-695.
[7] The Mammographic Image Analysis Society, Digital Mammography
Database ver 1.2,
(http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html).
[8] Wirth W., Nikitenko D., Lyon J., Segmentation of Breast Region in
Mammograms using a Rule-Based Fuzzy Reasoning Algorithm. ICGSTGraphics,
Vision and Image Processing Journal, 2005; 5(2); pp 45-54.
[9] Luc Vincent. Morphological Area Opening and Closing for Grayscale
Images, Proc. NATO Shape in Picture Workshop, Driebergen, The
Netherlands, Springer-Verlag, pp. 197-208, September 1992.
[10] Soille P., Morphological Image Analysis: Principles and Applications.
Springer-Verlag. 2003.
[11] Wirth M.A., Lyon J., Nikitenko N., Stapinski A. Removing Radioopaque
Artifacts from Mammograms using Area Morphology. In Proc. of SPIE
Medical Imaging: Image Processing. pp 1054-1065. 2004.
[12] Salembier P., Serra J. Flat Zones Filtering, Connected Operators, and
Filters by Reconstruction. IEEE Transaction on Image Processing, 4(8),
pp 1153-1160. 1995.
[13] Hoyer A., Spiesberg W., Computerized mammogram processing. In:
Phillips Technical Review. Vol. (38), pp 347-355, 1979.
[14] Lau T., Bischoff W., Automated Detection of Breast Tumours Using the
Asymmetry Approach. In. Computers and Bio-Medical Research. Vol.
(24), pp 273-295, 1991.
[15] Bick U., Giger M. L., Schmidt R.A., Nishikawa R.M., Wolverton D.E.,
Doi K., Automated Segmentation of Digitized Mammograms. Academic
Rediology, Vol. 2(2), pp 1-9, 1995.
[16] Masek M., Attikiouzel Y., Skin-Air Interface Extraction from
Mammograms Using an Automatic Local Thresholding Algorithm. In;
ICB Brono CR, pp 204-206, 2000.
[17] Abdel-Mottaleb M., Carman C.S., Hill C.R., Vafai S., Locating the
Boundary Between the Breast Skin Edge and the Background in
Digitized Mammograms. In Porc. Of the 3rd International Workshop on
Digital Mammography, pp 467-470, 1996.
[18] Mendez A.J., Tahoces P.J., Lado M.J., Souto M., Correa J.L., Vidal J.J.,
Automatic Detection of Breast Boarder and Nipple in Mammograms.
Computer Methods and Programs in Bio-Medicine, 49, pp 253-262,
1996.
[19] Karssemeijer N., te Brake G., Combining Single View Features and
Asymmetry for Detection of Mass Lesions. In IWDM, pp 1107-1108,
1998.
[20] Chandrasekhar R., Attikiouzel Y., Automatic Breast Boarder
Segmentation by Background Modelling and Subtraction. In Proc. of the
5th International Workshop on Digital Mammography, pp 560-565, 2000.
[21] Wirth M.A., Stapinski A., Segmentation of the Breast Region in
Mammograms using Active Contours. In Proc. of Visual Communication
and Image Processing, 5510, pp 1995-2006, 2003.
@article{"International Journal of Medical, Medicine and Health Sciences:59192", author = "Roshan Dharshana Yapa and Koichi Harada", title = "Breast Skin-Line Estimation and Breast Segmentation in Mammograms using Fast-Marching Method", abstract = "Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
", keywords = "Mammogram, fast marching method, mathematical morphology.", volume = "1", number = "5", pages = "275-9", }
