Abstract
In this paper, an optimization algorithm, utilizing a component measure of entropy, is developed for automatically tuning segmentation of mammograms by the Statistical Region Merging technique. The aim of this paper is to improve the mass segmentation in dense backgrounds. The proposed algorithm is tested on a database of 89 mammograms of which 41 have masses localized in dense background and 48 have masses in non-dense background. The algorithm performance is evaluated in conjunction with six standard enhancement techniques: Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE, Adaptive Clip Limit CLAHE based on standard deviation and Adaptive Clip Limit CLAHE based on standard entropy measure. For a comparison study, same experiments are performed using Fuzzy C-means Clustering technique. The experimental results show that the automatic tuning of SRM segmentation has the potential to produce an accurate segmentation of masses located in dense background while not compromising the performance on masses located in non-dense background.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization (WHO): WHO position paper on mammography screening. World Health Organization (WHO), Geneva (Switzerland) (2014)
Abbas, Q., Celebi, M.E., Garcia, I.F.: Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system. Biomed. Sig. Process. Control 8(2), 204–214 (2013)
Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 18(9), 1921–1935 (2009)
Bajger, M., Lee, G.N., Caon, M.: 3D segmentation for multi-organs in CT images. Electron. Lett. Comput. Vis. Image Anal. 12(2), 13–27 (2013)
Bajger, M., Ma, F., Williams, S., Bottema, M.: Mammographic mass detection with statistical region merging. In: 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 27–32. Syndey (2010)
Caon, M., Sedlar, J., Bajger, M., Lee, G.: Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry. Australas. Phys. Eng. Sci. Med. 37(2), 393–403 (2014)
Castellano, C.R., Nunez, C.V., Boy, R.C., et al.: Impact of mammographic breast density on computer-assisted detection (CAD) in a breast imaging department. Radiologia 53, 456–461 (2011)
Celebi, M.E., Kingravi, H.A., Iyatomi, H., et al.: Border detection in dermoscopy images using statistical region merging. Skin Res. Technol. 14(3), 347–353 (2008)
Guoa, Y., Cheng, H.D.: New neutrosophic approach to image segmentation. Pattern Recogn. 42, 587–595 (2009)
Heath, M., Bowyer, K., Kopans, D., et al.: The digital database for screening mammography. In: Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001)
Lee, G.N., Bajger, M.: Statistical temporal changes for breast cancer detection: a preliminary study. In: Fujita, H., Hara, T., Muramatsu, C. (eds.) IWDM 2014. LNCS, vol. 8539, pp. 635–642. Springer, Heidelberg (2014)
Morton, M.J., Whaley, D.H., Brandt, K.R., et al.: Screening mammograms: interpretation with computer-aided detection prospective evaluation. Radiology 239, 204–212 (2006)
Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)
Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 2, 87–110 (2010)
Sajeev, S., Bajger, M., Lee, G.: Segmentation of breast masses in local dense background using adaptive clip limit-CLAHE. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), DICTA 2015, pp. 669–676. IEEE (2015)
Zhang, H., Fritts, J.E., Goldman, S.A.: An entropy-based objective evaluation method for image segmentation. In: Proceedings of SPIE: Storage and Retrieval Methods and Applications for Multimedia, vol. 5307, pp. 38–49 (2004)
Acknowledgments
The authors would like to thank Dr. Peter Downey, clinical radiologist of BreastScreen SA for validating the core mass contours and valuable comments and discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sajeev, S., Bajger, M., Lee, G. (2016). Improving Breast Mass Segmentation in Local Dense Background: An Entropy Based Optimization of Statistical Region Merging Method. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_79
Download citation
DOI: https://doi.org/10.1007/978-3-319-41546-8_79
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41545-1
Online ISBN: 978-3-319-41546-8
eBook Packages: Computer ScienceComputer Science (R0)