Improving Breast Mass Segmentation in Local Dense Background: An Entropy Based Optimization of Statistical Region Merging Method

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• 关键词：Statistical Region Merging ; Dense background ; Enhancement ; Entropy ; Segmentation ; Mammography
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9699
• 期：1
• 页码：635-642
• 全文大小：780 KB
• 参考文献：1.World Health Organization (WHO): WHO position paper on mammography screening. World Health Organization (WHO), Geneva (Switzerland) (2014)
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  11.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)
  12.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)CrossRef
  13.Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)CrossRef
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  15.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)
  16.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)
  
• 作者单位：Shelda Sajeev (16)
  Mariusz Bajger (16)
  Gobert Lee (16)
  
  16. School of Computer Science, Engineering and Mathematics, Medical Device Research Institute, Flinders University, Adelaide, SA, 5001, Australia
  
• 丛书名：Breast Imaging
• ISBN：978-3-319-41546-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9699

文摘

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.