Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and segmenting regions of interest (ROIs) within the breast. Using prior manual segmentations performed by domain experts as ground truth data, we apply our method to 150 film mammograms with significant acquisition noise from the University of South Florida’s Digital Database for Screening Mammography. We then apply a similar segmentation procedure to detect the position and extent of suspicious regions of interest. Our approach was able to segment the breast profile from all 150 images, leaving minor residual noise adjacent to the breast in three. Performance on ROI extraction was also excellent, with 81% sensitivity and 0.96 false positives per image when measured against manually segmented ground truth ROIs. When not utilizing image morphology, our approach ran in linear time with the input size. These results highlight the potential of WaveCluster as a useful addition to the mammographic segmentation repertoire.
References
[1]
Lee, S.; Lo, C.; Wang, C.; Chung, P.; Chang, C.; Yang, C.; Hsu, P. A computer-aided design mammography screening system for detection and classification of microcalcifications. Int. J. Med. Inform. 2000, 60, 29–57, doi:10.1016/S1386-5056(00)00067-8.
[2]
Oliver, A.; Freixenet, J.; Martí, R.; Pont, J.; Pérez, E.; Denton, E.; Zwiggelaar, R. A novel breast tissue density classification methodology. IEEE Trans. Inform. Technol. Biomed. 2008, 12, 55–65, doi:10.1109/TITB.2007.903514.
[3]
Torrent, A.; Bardera, A.; Oliver, A.; Freixenet, J.; Boada, I.; Feixes, M.; Martí, R.; Lladó, X.; Pont, J.; Pérez, E.; et al. Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques. In Proceedings of the 9th International Workshop on Digital Mammography, Tucson, AZ, USA, 20–23 July 2008; pp. 9–16.
[4]
Khuzi, A.M.; Besar, R.; Zaki, W.W.; Ahmad, N.N. Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed. Imaging Interv. J. 2009, 5, doi:10.2349/biij.5.3.e17.
[5]
Alhadidi, B.; Zu’bi, M.H.; Suleiman, H.N. Mammogram breast cancer image detection using image processing functions. Inf. Technol. J. 2007, 6, 217–221, doi:10.3923/itj.2007.217.221.
[6]
Sheikholeslami, G.; Chatterjee, S.; Zhang, A. WaveCluster: A wavelet-Based clustering approach for spatial data. VLDB J. 2000, 8, 289–304, doi:10.1007/s007780050009.
[7]
Barnathan, M. Mining Complex High-Order Datasets. Dissertation. Ph.D. Thesis, Temple University, Philadelphia, PA, USA, April 2010.
[8]
Oliver, A. Automatic Mass Segmentation in Mammographic Images. Ph.D. Thesis, Universitat de Girona, Girona, Spain, July 2007.
[9]
Sholl, D.A. Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat. 1953, 87, 387–406.
[10]
Guliato, D.; Rangayyan, R.M.; Carvalho, J.D.; Santiago, S.A. Polygonal modeling of contours of breast tumors with the preservation of spicules. IEEE Trans. Biomed. Eng. 2008, 55, 14–20, doi:10.1109/TBME.2007.899310.
[11]
Nagi, J.; Abdul Kareem, S.; Nagi, F.; Khaleel Ahmed, S. Automated Breast Profile Segmentation for ROI Detection Using Digital Mammograms. In Proceedings of2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia, 30 November–2 December 2010; pp. 87–92.
[12]
Heath, M.; Bowyer, K.; Kopans, D.; Moore, R.; Kegelmeyer, W.P. The Digital Database for Screening Mammography. In Proceedings of the Fifth International Workshop on Digital Mammography, Toronto, Canada, June 2000; Medical Physics Publishing: Madison, WI, USA; pp. 212–218.
