Abstract
Mammography plays a significant role in the early detection of breast cancers since it can demonstrate changes in the breast, years before a patient or physician can feel them. The research work conducted in the research paper highlights the process of segmentation and classification of mammogram images intending to detect the presence of tumors in the breast at early stages and classifying it as benign (cancerous) or malignant (non-cancerous) so that the course of treatment could be decided to prevent further damage. The flowchart developed in the research paper defines a systematic approach adopted to perform segmentation on mammograms. This includes the use of techniques like Green Channel Complement, CLAHE (Contrast Limited Adaptive Histogram Equalization), Morphological operations, and FCM (Fuzzy C-Means). Mammogram images from the MIAS (Mammographic Image Analysis Society) database have been used for performing segmentation. The research paper features a detailed algorithm that discusses the detailed adopted approach. The GUI (Graphical User Interface) has been constructed with multiple windows to show the output received at each step after appropriate processing. The results have been obtained in the form of numerical readings using performance evaluation parameters like sensitivity, specificity, accuracy, positive predictive value, negative predictive value, false-negative rate, false-positive rate, etc. The obtained readings of different parameters prove the authenticity of the conducted work. Segmentation enables the scrutinizing of any region within an image. The conducted research work can prove helpful in enhancing the mammogram image and focusing on the segmented image which indicates the presence of microcalcifications. The effectively conducted segmentation enables the radiologist to classify the tumor and monitor the seriousness of caused damage. Based on the obtained results the further treatment of the patient can be decided upon.
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References
Saidin N, Sakim HAM, Ngah UK, Shuaib IL. Segmentation of breast regions in mammogram based on density: a review. Int J Comput Sci. 2012;9(4):108–16.
Zheng B, Sumkin JH, Zuley ML, Wang X, Klym AH, Gur D. Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol. 2012;80(11):3222–8.
Khalvati F, Gallego-Ortiz C, Balasingham S, Martel AL. Automated segmentation of breast in 3-D MR images using a robust atlas. IEEE Trans Med Imaging. 2015;34:116–25.
Gubern-Merida A, Kallenberg M, Mann RM, Marti R, Karssemeijer N. Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE J Biomed Health Inform. 2015;19(1):349–57.
Llobet R, Pollan M, Anton J. Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Comput Methods Programs Biomed. 2014;116(2):105–15.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition; 2015. http://arxiv.org/abs/1409.1556.
He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on IMAGENET classification. Proc IEEE Int Conf Comput Vis. 2015. 1026–34.
Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng. 2013;15:327–57.
Moreira IC. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236–48.
Kozegar E, et al. Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther. 2013;9(4):592–600.
Dhungel N, Carneiro G, Bradley AP. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal. 2017;37:114–28.
Becker A. Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol. 2017;52:434–40.
Lee RS, et al. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 2017;4:170–7.
Te Brake GM, Karssemeijer N, Hendriks JH. An automatic method to discriminate malignant masses from normal tissue in digital mammograms. Phys Med Biol. 2000;45(10):2843–57.
Campanini R, et al. A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys Med Biol. 2004;49(6):961–75.
Chen Z, Zwiggelaar R. Segmentation of the breast region with pectoral muscle removal in mammograms. Med Image Understand Anal. 2010; 71–76.
Makandar A, Halalli B. Breast cancer image enhancement using median filter and CLAHE. Int J Sci Eng Res. 2015;6(4):462–5.
Mustra M, Grgic M, Rangayyan RM. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput. 2015; 1–22.
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This article is part of the topical collection “Applications of Cloud Computing, Data Analytics and Building Secure Networks” guest edited by Rajnish Sharma, Pao-Ann Hsiung and Sagar Juneja.
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Singh, B., Kaur, R., Kaur, A. et al. Discoursing Novel Procedure for Segmentation and Classification of Mammograms. SN COMPUT. SCI. 2, 61 (2021). https://doi.org/10.1007/s42979-021-00454-6
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DOI: https://doi.org/10.1007/s42979-021-00454-6