Abstract
Breast cancer is the most routinely identified carcinoma among women in India, and it is one of the foremost causes of cancer death in women. Radiologists prefer mammograms for visualizing breast cancer. Different types of noises including Gaussian noise and salt-and-pepper noise affect the mammograms leading to inaccurate classification. Mammograms consist of numerous artifacts too, which depressingly affect the finding of breast cancer. The existence of pectoral muscles makes anomaly finding a cumbersome task. The recognition of glandular tissue in mammograms is vital in assessing asymmetry between left and right breasts and in conjecturing the radiation risk associated with screening. Thus, the proposed method focuses on improving the segmentation accuracy of noisy mammograms. It involves preprocessing which includes denoising using a pretrained convolutional neural network, artifacts removal using thresholding, and modified region growing and enhancement using two-stage adaptive histogram equalization along with segmentation of mammogram images into sections conforming to different densities using K-means clustering. The projected method has been confirmed on the Mini-MIAS database with ground truth provided by expert radiologists. The results illustrate that the proposed method is competent in eradicating noise and pectoral muscles without degrading quality and contrast and in fragmenting different denoised mammograms into different mammographic densities with high accuracy.
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References
Agarwal R et al (2019) Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging. https://doi.org/10.1117/1.jmi.6.3.031409
Asante-Mensah MG, Cichocki A (2019) Medical image de-noising using deep networks. In: IEEE international conference on data mining workshops, ICDMW. https://doi.org/10.1109/ICDMW.2018.00052
Bray F et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. https://doi.org/10.3322/caac.21492
Bray F et al (2013) Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer. https://doi.org/10.1002/ijc.27711
Gandomkar Z et al (2019) BI-RADS density categorization using deep neural networks. Presented at the. https://doi.org/10.1117/12.2513185
Ghosh SK et al (2020) Restoration of mammograms by using deep convolutional denoising auto-encoders. Presented at the. https://doi.org/10.1007/978-981-13-8676-3_38
Gu S et al (2014) Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2014.366
Jain V, Seung HS (2009) Natural image denoising with convolutional networks. In: Advances in neural information processing systems 21—proceedings of the 2008 conference
Jifara W et al (2019) Medical image denoising using convolutional neural network: a residual learning approach. J. Supercomput. https://doi.org/10.1007/s11227-017-2080-0
Kallenberg M et al (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2016.2532122
Krizhevsky A et al (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems
Lee S et al (2019) Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study. Optik (Stuttg). https://doi.org/10.1016/j.ijleo.2018.11.167
Liu Z et al (2013) A robust region-based active contour model with point classification for ultrasound breast lesion segmentation. In: Medical imaging 2013: computer-aided diagnosis. https://doi.org/10.1117/12.2006164
Mao XJ et al (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Adv Neural Inf Proces Syst
Marrocco C et al (2018) Mammogram denoising to improve the calcification detection performance of convolutional nets. Presented at the. https://doi.org/10.1117/12.2318069
Mohamed AA et al (2018) A deep learning method for classifying mammographic breast density categories. Med Phys. https://doi.org/10.1002/mp.12683
Mughal B et al (2018) Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer. https://doi.org/10.1186/s12885-018-4638-5
Oiwa M et al (2019) Can quantitative evaluation of mammographic breast density, “volumetric measurement”, predict the masking risk with dense breast tissue? Investigation by comparison with subjective visual estimation by Japanese radiologists. Breast Cancer. https://doi.org/10.1007/s12282-018-0930-0
Oliver A et al (2015) Breast density analysis using an automatic density segmentation algorithm. J Digit Imaging. https://doi.org/10.1007/s10278-015-9777-5
Parmar C et al (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE. https://doi.org/10.1371/journal.pone.0102107
Pavan ALM, de Oliveira M, Alvarez M, Sampaio AJM, Trindade AP, Duarte SB, de Pina DR (2016) Breast tissue segmentation by fuzzy C-means. Phys Medica 32:336
Pavan ALM et al (2019) Automatic identification and extraction of pectoral muscle in digital mammography. In: IFMBE Proceedings. https://doi.org/10.1007/978-981-10-9035-6_27
Peng J et al (2015) 3D liver segmentation using multiple region appearances and graph cuts. Med Phys. https://doi.org/10.1118/1.4934834
Pereira S et al (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2016.2538465
Release P (2013) Latest world cancer statistics global cancer burden rises to 14:1 million new cases in 2012: marked increase in breast cancers must be addressed
Saidin N, Sakim HAM, Ngah UK, Shuaib IL (2012) Segmentation of breast regions in mammogram based on density: a review. Int J Comput Sci Issues 9(4): 104
Salman NH, Ali SIM (2019) Mammograms segmentation and extraction for breast cancer regions based on region growing. Baghdad Coll Econ Sci Univ 57:448–460
Shen R et al (2018) Automatic pectoral muscle region segmentation in mammograms using genetic algorithm and morphological selection. J Digit Imaging. https://doi.org/10.1007/s10278-018-0068-9
Shinde V, Thirumala Rao B (2019) Novel approach to segment the pectoral muscle in the mammograms. Adv Intell Syst Comput https://doi.org/10.1007/978-981-13-0617-4_22
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International conference for learning representations
Singh G et al (2019) Deep convolution neural network based denoiser for mammographic ımages. Presented at the https://doi.org/10.1007/978-981-13-9939-8_16
Suckling J et al (1994) The mammographic image analysis society digital mammogram database. Expert Medica Int Congr Ser
Sun C et al (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med. https://doi.org/10.1016/j.artmed.2017.03.008
Suzuki K et al (2010) CT liver volumetry using geodesic active contour segmentation with a level-set algorithm. In: Medical Imaging 2010: Computer-Aided Diagnosis. https://doi.org/10.1117/12.843950
Wu W et al (2016) Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts. Comput Math Methods Med. https://doi.org/10.1155/2016/9093721
Yin K et al (2019) A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-018-1867-7
Zhang K et al (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process. https://doi.org/10.1109/TIP.2017.2662206
Zhang XP (2001) Thresholding neural network for adaptive noise reduction. IEEE Trans Neural Networks. https://doi.org/10.1109/72.925559
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Dabass, J., Dabass, M. (2021). Segmentation of Noisy Mammograms Using Hybrid Techniques. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_104
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