Identification of the skin-air interface in CC- and MLO-view mammograms via computational intelligence techniques
Pages 250 - 255
Abstract
Identification of the skin-air interface is a challenging, yet essential task in mammographic image analysis. Two strategies for determining this boundary are presented in this study. The stroma edge is initially located using a K-clustered self-organizing map and morphological operations. The first procedure then applies a thresholding technique near the stroma edge to identify the skin line, while the second supposes that the distance between the stroma edge and the skin line is approximately equal throughout the image. Applied to 500 mammograms from the Digital Database for Screening Mammography (DDSM), qualitative findings indicate that the mammographic projection determined which approach was most effective. Specifically, medio-lateral oblique (MLO) view mammograms favored the first approach and the cranio-caudal (CC) view favored the second, regardless of mammographic density. To confirm the viability of this method as a pre-processing step for computer-aided diagnosis (CAD), the resulting segmentation was compared to the location of abnormalities when present. In this data set, 218 mammograms contained abnormalities, annotated by a radiologist. Four of these were technically suboptimal, while 214 (98.17%) were completely encompassed by the segmentation.
References
[1]
Ferrari, R., Rangayyan, R., Desautels, J., Borges, R. and Frere, A. 2004. Identification of the breast boundary in mammograms using active contour models. Medical & biological engineering & computing, 42, 2, 201--208.
[2]
Gonzalez, R. C. and Woods, R. E. 2008. Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
[3]
Heath, M., Bowyer, K., Kopans, D., Moore, R. and Kegelmeyer, P. 2000. The Digital Database for Screening Mammography. In Proceedings of the 5th Int. Workshop on Digital Mammography (Toronto, Canada), 212--218.
[4]
Kohonen, T. 1990. The self-organizing map. Proceedings of the IEEE, 78, 9, 1464--1480.
[5]
Liasis, G. and Petroudi, S. 2011. Estimation of the skin-air interface in mammograms using level sets. In Proceedings of the 1st Middle East Conference on Biomedical Eng. (MECBME), 2011 21-24 Feb. 2011). 239--242.
[6]
NCI and NIH. 2011. Breast Cancer Screening (PDQ®, Physician Data Query).
[7]
Ojala, T., Näppi, J. and Nevalainen, O. 2001. Accurate segmentation of the breast region from digitized mammograms. Computerized Medical Imaging and Graphics, 25, 47--59.
[8]
Rickard, H. E. The Effectiveness of Self-Organizing Maps for Mammographic Image Analysis. Dissertation, University of Louisville, 2008.
[9]
Rickard, H. E., Tourassi, G. D., Eltonsy, N. and Elmaghraby, A. S. 2004. Breast segmentation in screening mammograms using multiscale analysis and self-organizing maps. In Proceedings of the 26th Int. Conf. of the IEEE Eng. in Medicine and Biology Society, 2004. IEMBS '04. 1-5 Sept. 2004). 1786--1789.
[10]
Sun, Y., Janer, R., Suri, J., Ye, Z. and Rangayyan, R. M. 2005. Effect of adaptive-neighborhood contrast enhancement on the extraction of the breast skin-line in mammograms. In Proceedings of the 27th Int. Conf. of the Eng. in Medicine and Biology Society (EMBC 2005) 3475--3478.
[11]
Thiruvenkadam, S., Acharyya, M., Neeba, N. V., Jhunjhunwala, P. and Ranjan, S. 2010. A region-based active contour method for extraction of breast skin-line in mammograms. In Proceedings of the IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, 2010, 189--192.
[12]
Wei, K., Guangzhi, W. and Hui, D. 2005. Segmentation of the breast region in mammograms using watershed transformation. In Proceedings of the IEEE 27th Int. Conf. of the Eng. in Medicine and Biology Society (EMBS 2005), 6500--6503.
[13]
Wirth, M., Lyon, J., Fraschini, M. and Nikitenko, D. 2004. The effect of mammogram databases on algorithm performance. In Proceedings of the 17th IEEE Symp. on Computer-Based Medical Systems (CBMS 2004), 15--20.
Index Terms
- Identification of the skin-air interface in CC- and MLO-view mammograms via computational intelligence techniques
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Published In
March 2012
424 pages
ISBN:9781450312035
DOI:10.1145/2184512
- Conference Chair:
- Randy K. Smith,
- Program Chair:
- Susan V. Vrbsky
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Published: 29 March 2012
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