Self-Organization Map Based Segmentation of Breast Cancer
Author : A. Arokiyamary Delphina, M. Kamarasan and S. SathiamoorthyVolume 7 No.2 July-December 2018 pp 31-36
Abstract
Breast cancer is second major leading cause of cancer fatality in women. Mammography prevails best method for initial detection of cancers of breast, capable of identifying small pieces up to two years before they grow large enough to be evident on physical testing. X-ray images of breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, Radio-graphic images into regions of similar texture is usually performed during method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and implied transition from one texture to makes segmentation remarkably hard. The task of analyzing different texture areas can be considered form of exploratory report since priori awareness about number of different regions in image is not known. This paper presents a segmentation method by utilizing SOM.
Keywords
Breast Cancer, Mammography, Self-Organizing Map, Euclidean Distance, Validity Measure, Double Bouldin Index
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References
[1] Williams, B.Lovoria, et al., “Demographic, psychosocial, and behavioral associations with cancer screening among a homeless population,” Public Health Nursing, 2018.
[2] Henriksen, L.Emilie, et al., “The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review,” ActaRadiologica, 2018, 0284185118770917.
[3] Siddharth Singh Chouhan, Ajay Kaul, and UdayPratap Singh, “Image Segmentation Using Computational Intelligence Techniques: Review”, Archives of Computational Methods in Engineering, 2018.
[4] Ahmed, O.Isra, Banazier A. Ibraheem, and Zeinab A. Mustafa, “Detection of Eye Melanoma Using Artificial Neural Network,” Journal of Clinical Engineering, Vol. 43, No. 1, pp. 22-28, 2018.
[5] Shukla, and Nagesh, et al., “Breast cancer data analysis for survivability studies and prediction,” Computer Methods and Programs in Biomedicine, Vol. 155, pp.199-208, 2018.
[6] Arora, Shaveta, MadasuHanmandlu, and Gaurav Gupta, “Filtering impulse noise in medical images using information sets,” Pattern Recognition Letters, 2018.
[7] Boemer, Fabian, Edward Ratner, and AmauryLendasse, “Parameter-free image segmentation with SLIC,” Neurocomputing, Vol. 277, pp. 228-236, 2018.
[8] Park, and Young-Seuk, et al., “Multivariate Data Analysis by Means of Self-Organizing Maps,” Ecological Informatics. Springer, Cham, pp. 251-272, 2018.
[9] Kumar, Krishan, Deepti D. Shrimankar, and Navjot Singh, “Eratosthenes sieve based key-frame extraction technique for event summarization in videos,” Multimedia Tools and Applications, Vol. 77, No. 6, pp. 7383-7404, 2018.
[10] Ngo, Long Thanh, Trong Hop Dang, and WitoldPedrycz, “Towards Interval-Valued Fuzzy Set–based Collaborative Fuzzy Clustering Algorithms,” Pattern Recognition, 2018.