Abstract
Mammogram image segmentation plays a crucial role in detecting the lesion region in the breast masses. In this context, the key challenging issue is the false positive detection of pectoral muscles or fatty tissues as the lesion region. Further, the presence of noise and imaging errors degrade the segmentation accuracy. To address these problems, we suggest an Error Sensitive Fuzzy Clustering (ESFC) technique for delineating the different tissue regions in the mammogram images. The basic idea is to incorporate an error sensitive regulating factor in the objective function of the Fuzzy C-means (FCM) algorithm for enhancing the clustering performance in the noisy environment. The suggested technique is experimented with multiple volumes of mammogram images from standard databases. State-of-the-art methods are compared. Quantitative assessment is done using standard evaluation indices. The results indicate better quality with the proposed method.
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References
WHO Report on Breast Cancer: https://www.who.int/news-room/fact-sheets/detail/breast-cancer#:~:text=In%202020%2C%20there%20were%202.3,the%20world's%20most%20prevalent%20cancer. Accessed in May 2022
Rositch, A.F., Unger-Saldana, K., DeBoer, R.J., Ng’ang’a, A., Weiner, B.J.: The role of dissemination and implementation science in global breast cancer control programs: Frameworks, methods, and examples. Cancer 126(Suppl 10), 2394–2404 (2020). http://www.ncbi.nlm.nih.gov/pubmed/32348574
Michael, E., Ma, H., Li, H., Kulwa, F., Li, J.: Breast cancer segmentation methods: current status and future potentials. BioMed Res. Int. 2021, 1–29 (2021). https://doi.org/10.1155/2021/9962109
Bezdek, J.C., Hall, L.O., Clark, L.P.: Review of MR segmentation technique in pattern recognition. Med. Phys. 10(20), 33–48 (1993)
Kannan, S.R., Ramathilagam, R., Devi, R., Sathya, A.: Robust kernel FCM in segmentation of breast medical images. Expert Syst. Appl. 38, 4382–4389 (2011)
Das, A., Sabut, S.K.: Kernelized fuzzy C-means clustering with adaptive thresholding for segmenting liver tumors. Procedia Comput. Sci. 92, 389–395 (2016)
Yang, M.S., Tsai, H.S.: A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recogn. Lett. 29, 1713–1725 (2008)
Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraint based on new kernel induced distance measure. IEEE Trans. Syst., Man Cybern. B 34(4), 1906–1917 (2004)
Atanassov, K.T.: Atanassov’s, Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, 983 (Deposed in Central Science – Technology Library of Bulgaria Academy of Science- 1697/84)
Chaira, T., Panwar, A.: An Atanassov’s intuitionistic Fuzzy Kernel Clustering for medical image segmentation. Int. J. Comput. Intell. Syst. 7, 360–370 (2013)
Verma, H., Gupta, A., Kumar, D.: Modified intuitionistic fuzzy c means algorithm incorporating hesitation degree. Pattern Recogn. Lett. 122, 45–52 (2019)
Xu, Z., Wu, J.: Intuitionistic fuzzy C-means clustering algorithms. J. Syst. Eng. Electron. 42(4), 580–590 (2010)
Kumar, S.A., Harish, B.S.: A modified intuitionistic fuzzy clustering algorithm for medical image segmentation. J. Intell. Syst. 27(4), 593–607 (2018)
Chaira, T.: An intuitionistic fuzzy clustering approach for detection of abnormal regions in mammogram images. J. Digit. Imaging 34(2), 428–439 (2021)
Gudbjartsson, H., Patz, S.: The rician distribution of noisy MRI data. Magn. Reson. Med. 34(6), 910–914 (1995)
Rebecca, S.L., Francisco, G., Assaf, H., Daniel, R.: Curated Breast Imaging Subset of DDSM. The Cancer Imaging Archive (2016). https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY
Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad Radiol. 19(2), 236–248 (2012)
Suckling, J., et al.: Mammographic Image Analysis Society (MIAS) database v1.21 (2015). https://www.repository.cam.ac.uk/handle/1810/250394
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Chaudhary, B.K., Agrawal, S., Mishro, P.K., Panda, R. (2023). An Error Sensitive Fuzzy Clustering Technique for Mammogram Image Segmentation. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_28
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DOI: https://doi.org/10.1007/978-3-031-27440-4_28
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