A REVIEW ON IMAGE PROCESSING TECHNIQUES FOR BREAST CANCER ANALYSIS
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Volume 4 (2), December 2021, Pages 206-231
Iqra Rashid, Javeria Naz
In recent years breast cancer detection has been the most popular research topic in medical image analysis. It is the most common malignancy in women, and men can also be affected. Conferring to the American Cancer Society, in 2019, almost two million new cases were registered, and the death rate was almost 41,000. The death rate can be reduced if the cancer is timely diagnosed. For cancer detection, different modalities are used, like MRI, ultrasound, and mammography. The most common and popular modality is mammography. A mammogram shows breast irregularities that are benign or malignant. In digital mammography, it is not easy to extract accurate breast regions. The main problem in the extraction region of concern is pectoral muscle suppression. The pectoral muscle appears in the breast area. Sometimes it is marked as an area of attention that causes a false positive rate. It is essential to eradicate pectoral muscles from the breast. This manuscript overviews the introduction of basic breast cancer terminologies. The work also analyzes state-of-the-insight imaging procedures used for breast cancer analysis.
Breast cancer, Mammogram, Pectoral muscle, Segmentation and Classification.
Abdel-Nasser, M., Moreno, A., Rashwan, H. A., & Puig, D. (2017). Analyzing the evolution of breast tumors through flow fields and strain tensors. Pattern Recognition Letters, 93, 162-171.
Abdulla, S. H., Sagheer, A. M., & Veisi, H. (2021, December). Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images. In MENDEL (Vol. 27, No. 2, pp. 36-43).
Abdullah-Al-Wadud, M., Kabir, M. H., Dewan, M. A. A., & Chae, O. (2007). A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(2), 593-600.
Akila, K., Jayashree, L. S., & Vasuki, A. (2015). Mammographic image enhancement using indirect contrast enhancement techniques–a comparative study. Procedia Computer Science, 47, 255-261.
Akram, M., Iqbal, M., Daniyal, M., & Khan, A. U. (2017). Awareness and current knowledge of breast cancer. Biological research, 50(1), 1-23.
Al-Khalidi, F. Q., Alkindy, B., & Abbas, T. (2019). Extract the breast cancer in mammogram images. Technology, 10(02), 96-105.
Amin, J., et al. (2020). Brain tumor detection by using stacked autoencoders in deep learning. Journal of medical systems, 44(2), 1-12.
Amin, J., et al. (2020b). Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Computing and Applications, 32(20), 15965-15973.
Amin, J., et al. (2020c). Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cognitive Systems Research, 59, 304-311.
Amin, J., et al. (2021). 3d semantic deep learning networks for leukemia detection. Computers, Materials & Continua, 69(1), 785-799
Amin, J., et al. (2022). Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model. Microscopy Research and Technique. 85(5), 1926-1936.
Amin, J., et al. (2022a). Malaria Parasite Detection Using a Quantum-Convolutional Network. Computers, Materials & Continua, 70(3), 6023 - 6039
Amin, J., Sharif, M., Raza, M., & Yasmin, M. (2018). Detection of brain tumor based on features fusion and machine learning. Journal of Ambient Intelligence and Humanized Computing, 1-17.
Amin, J., Sharif, M., Raza, M., Saba, T., & Anjum, M. A. (2019). Brain tumor detection using statistical and machine learning method. Computer methods and programs in biomedicine, 177, 69-79.
Amin, J., Sharif, M., Raza, M., Saba, T., & Rehman, A. (2019, April). Brain tumor classification: feature fusion. In 2019 international conference on computer and information sciences (ICCIS) (pp. 1-6). IEEE.
Amin, J., Sharif, M., Rehman, A., Raza, M., & Mufti, M. R. (2018). Diabetic retinopathy detection and classification using hybrid feature set. Microscopy research and technique, 81(9), 990-996.
Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018). Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87, 290-297.
Amin, J., Sharif, M., Yasmin, M., Saba, T., & Raza, M. (2020). Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions. Multimedia Tools and Applications, 79(15), 10955-10973.
Andria, G., Attivissimo, F., Cavone, G., Giaquinto, N., & Lanzolla, A. M. L. (2012). Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Measurement, 45(7), 1792-1800.
Angadi, S. A., & Kodabagi, M. M. (2013, August). A fuzzy approach for word level script identification of text in low resolution display board images using wavelet features. In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1804-1811). IEEE.
Anitha, J., Peter, J. D., & Pandian, S. I. A. (2017). A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Computer methods and programs in biomedicine, 138, 93-104.
Arefan, D., et al. (2015). Automatic breast density classification using neural network. Journal of Instrumentation, 10(12), T12002.
Attique Khan, M., et al. (2021). A two‐stream deep neural network‐based intelligent system for complex skin cancer types classification. International Journal of Intelligent Systems.
Azimi, N., Azar, A., Khan, A., & DeBenedectis, C. M. (2019). Benign breast cyst in a young male. Cureus, 11(6).
Bahadure, N. B., Ray, A. K., & Thethi, H. P. (2017). Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. International journal of biomedical imaging, 2017.
Bajaj, V., et al. (2019). Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition. Neural Computing and Applications, 31(8), 3307-3315.
Bandyopadhyay, S. K. (2018). Overview Process for Identification of Breast Abnormalities. Oriental Journal of Computer Science and Technology, 11(3), 140-142.
Ben Rabeh, A., Benzarti, F., & Amiri, H. (2017). Segmentation of brain MRI using active contour model. International Journal of Imaging Systems and Technology, 27(1), 3-11.
Benign Breast Conditions (2019) Available: https://www.breastcancer.org/symptoms/benign
Bhateja, V., Misra, M., & Urooj, S. (2020). Region-Based and Feature Based Mammogram Enhancement Techniques. In Non-Linear Filters for Mammogram Enhancement (pp. 47-54). Springer, Singapore.
Bhatnagar, S., & Gupta, R. (2019). Denoising of Mammographic Images from Quantum Noise in Wavelet Domain. International Journal of Recent Technology and Engineering, 8, 435-440.
Boldbaatar, E. A., Lin, L. Y., & Lin, C. M. (2019). Breast tumor classification using fast convergence recurrent wavelet Elman neural networks. Neural Processing Letters, 50(3), 2037-2052.
Caballo, M., et al. (2020). Deep learning-based segmentation of breast masses in dedicated breast CT imaging: radiomic feature stability between radiologists and artificial intelligence. Computers in biology and medicine, 118, 103629.
Cadman, B. (2018). What happens at each stage of breast cancer? Available: https://www.medicalnewstoday.com/articles/322760.php
Chan, H. P., Samala, R. K., & Hadjiiski, L. M. (2019). CAD and AI for breast cancer—recent development and challenges. The British journal of radiology, 93(1108), 20190580.
Chan, N. H., Hasikin, K., & Kadri, N. A. (2019, March). An improved enhancement technique for mammogram image analysis: A fuzzy rule-based approach of contrast enhancement. In 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 202-206). IEEE.
Dabass, J., et al. (2019, March). Mammogram image enhancement using entropy and CLAHE based intuitionistic fuzzy method. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 24-29). IEEE.
Dhahri, H., et al. (2019). Automated breast cancer diagnosis based on machine learning algorithms. Journal of healthcare engineering, 2019.
Divyashree, B. V., et al. (2022). Segmentation of Pectoral Muscle in Mammograms Using Granular Computing. Journal of Information Technology Research (JITR), 15(1), 1-14.
Dong, M., et al. (2015, December). A new study on mammographic image denoising using multiresolution techniques. In Eighth International Conference on Machine Vision (ICMV 2015) (Vol. 9875, p. 987518). International Society for Optics and Photonics.
Eckert, D., et al. (2020). Deep learning-based denoising of mammographic images using physics-driven data augmentation. In Bildverarbeitung für die Medizin 2020 (pp. 94-100). Springer Vieweg, Wiesbaden.
Elahi, M. A., et al. (2013). Artifact removal algorithms for microwave imaging of the breast. Progress In Electromagnetics Research, 141, 185-200.
Elahi, M. A., et al. (2017). Adaptive artifact removal for selective multistatic microwave breast imaging signals. Biomedical Signal Processing and Control, 34, 93-100.
El-Sharkawy, et al. (2007). Mammaglobin: A novel tumor marker for breast cancer. Turkish J Cancer, 37, 89-97.
Fadhil, S. S., & Dawood, F. A. A. (2021). Automatic Pectoral Muscles Detection and Removal in Mammogram Images. Iraqi Journal of Science, 676-688.
Fayyaz, A. M., et al. (2022). Leaf Blights Detection and Classification in Large Scale Applications. Intelligent automation and soft computing, 31(1), 507-522.
Garg, N., & Garg, N. (2013). Binarization techniques used for grey scale images. International Journal of Computer Applications, 71(1), 8-11.
Gómez, K. A. H., et al. (2021). Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms. Healthcare Informatics Research, 27(3), 222-230.
Haider, W., Sharif, M., & Raza, M. (2011). Achieving accuracy in early stage tumor identification systems based on image segmentation and 3D structure analysis. Computer Engineering and Intelligent Systems, 2(6), 96-102.
Hamad, Y. A., Simonov, K., & Naeem, M. B. (2018, November). Breast cancer detection and classification using artificial neural networks. In 2018 1st Annual International Conference on Information and Sciences (AiCIS) (pp. 51-57). IEEE.
Hameed, M., et al. (2012). Framework for the comparison of classifiers for medical image segmentation with transform and moment based features. Research Journal of Recent Sciences, 2277, 2502.
Holland, J. H. K. (2019). A Comprehensive Guide to Breast Cancer. Available: https://www.healthline.com/health/breast-cancer
Houben, G., Fujita, S., Takahashi, K., & Fujii, T. (2019). Fast and Robust Disparity Estimation from Noisy Light Fields Using 1-D Slanted Filters. IEICE Transactions on Information and Systems, 102(11), 2101-2109.
Invasive Breast Cancer: Symptoms, Treatments, Prognosis. (2019) Available: https://www.webmd.com/breast-cancer/invasive-breast-cancer
Irum, I., Raza, M., & Sharif, M. (2012). Morphological techniques for medical images: A review. Research Journal of Applied Sciences, Engineering and Technology, 4(17), 2948-2962.
Irum, I., Shahid, M. A., Sharif, M., & Raza, M. (2015). A Review of Image Denoising Methods. Journal of Engineering Science & Technology Review, 8(5), 41-48.
Irum, I., Sharif, M., Raza, M., & Yasmin, M. (2014). Salt and pepper noise removal filter for 8-bit images based on local and global occurrences of Grey levels as selection indicator. Nepal Journal of Science and Technology, 15(2), 123-132.
Irum, I., Sharif, M., Yasmin, M., Raza, M., & Azam, F. (2014). A noise adaptive approach to impulse noise detection and reduction. Nepal Journal of Science and Technology, 15(1), 67-76.
Jen, C. C., & Yu, S. S. (2015). Automatic detection of abnormal mammograms in mammographic images. Expert Systems with Applications, 42(6), 3048-3055.
Joseph, A. J., & Pournami, P. N. (2021). Multifractal theory based breast tissue characterization for early detection of breast cancer. Chaos, Solitons & Fractals, 152, 111301.
Joseph, A. M., John, M. G., & Dhas, A. S. (2017, March). Mammogram image denoising filters: A comparative study. In 2017 Conference on emerging devices and smart systems (ICEDSS) (pp. 184-189). IEEE.
Kaur, P., Singh, G., & Kaur, P. (2019). Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics in Medicine Unlocked, 16, 100151.
Khan, M. A., et al. (2020). An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimedia Tools and Applications, 79(25), 18627-18656.
Khan, M. A., et al. (2020). Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition. Applied Soft Computing, 87, 105986.
Lal, M., et al. (2018). Study of face recognition techniques: a survey. International Journal of Advanced Computer Science and Applications, 9(6), 42-49.
Lbachir, I. A., Daoudi, I., & Tallal, S. (2021). Automatic computer-aided diagnosis system for mass detection and classification in mammography. Multimedia Tools and Applications, 80(6), 9493-9525.
Lee, R. S., et al. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific data, 4(1), 1-9.
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, 180, 569-575.
Li, H., Mukundan, R., & Boyd, S. (2021). Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms. Journal of Imaging, 7(10), 205.
Li, Y., Zhang, L., Chen, H., & Cheng, L. (2020). Mass detection in mammograms by bilateral analysis using convolution neural network. Computer methods and programs in biomedicine, 195, 105518.
Ling, Q., et al. (2018). Patch Based Grid Artifact Suppressing in Digital Mammography. BioMed Research International, 2018.
Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Lukashenko, A., et al. (2019). A Method for Extracting a Breast Image from a Mammogram Based on Binarization, Scaling and Segmentation. IDDM.
Maini, R., & Aggarwal, H. (2010). A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053.
Majeed, T. F., Al-Jawad, N., & Sellahewa, H. (2013, September). Breast border extraction and pectoral muscle removal in MLO mammogram images. In 2013 5th Computer Science and Electronic Engineering Conference (CEEC) (pp. 119-124). IEEE.
Makandar, A., & Halalli, B. (2015). Breast cancer image enhancement using median filter and CLAHE. International Journal of Scientific & Engineering Research, 6(4), 462-465.
Masood, S., Sharif, M., Masood, A., Yasmin, M., & Raza, M. (2015). A survey on medical image segmentation. Current Medical Imaging, 11(1), 3-14.
Masood, S., Sharif, M., Yasmin, M., Raza, M., & Mohsin, S. (2013). Brain image compression: a brief survey. Research Journal of Applied Sciences, Engineering and Technology, 5(1), 49-59.
Mayer, A. (2019, October). Neural Denoising of Ultra-low Dose Mammography. In Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (Vol. 11905, p. 215). Springer Nature.
Menhas, R., & Umer, S. (2015). Breast cancer among Pakistani women. Iranian journal of public health, 44(4), 586-7.
Moghbel, M., et al. (2020). A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artificial Intelligence Review, 53(3), 1873-1918.
Mughal, B., et al. (2018). Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC cancer, 18(1), 1-14.
Mughal, B., Muhammad, N., & Sharif, M. (2019). Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. International journal of medical informatics, 126, 26-34.
Mughal, B., Sharif, M., Muhammad, N., & Saba, T. (2018). A novel classification scheme to decline the mortality rate among women due to breast tumor. Microscopy research and technique, 81(2), 171-180.
Nasir, I. M., et al. (2022). HAREDNet: A deep learning based architecture for autonomous video surveillance by recognizing human actions. Computers & Electrical Engineering, 99, 107805.
Naz, J., et al. (2021). Detection and classification of gastrointestinal diseases using machine learning. Current Medical Imaging, 17(4), 479-490.
Naz, J., et al. (2021). Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Processing Letters, 1-26.
Naz, J., et al. (2021). Segmentation and classification of stomach abnormalities using deep learning. Computers, Materials & Continua, 69(1), 607-625.
Naz, M., et al. (2021). From ECG signals to images: a transformation based approach for deep learning. PeerJ Computer Science, 7, e386.
Odle, T. G. (2015). Breast imaging artifacts. Radiologic Technology, 87(1), 65M-87M.
Pawar, S. D., et al. (2021). Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification. Biocybernetics and Biomedical Engineering, 41(3), 1224-1241.
Pragathi, J., & Patil, H. T. (2013). Segmentation method for ROI detection in mammographic images using Wiener filter and Kittler’s method. In IJCA Proceedings on International Conference on Recent Trends in Engineering and Technology (pp. 27-33).
Radhi, E. A., & Kamil, M. Y. (2021). Breast tumor detection via active contour technique. International Journal of Intelligent Engineering and Systems, 14(4), 561-570.
Ramani, R., Vanitha, N. S., & Valarmathy, S. (2013). The pre-processing techniques for breast cancer detection in mammography images. International Journal of Image, Graphics and Signal Processing, 5(5), 47.
Rampun, A., et al. (2019). Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Medical image analysis, 57, 1-17.
Ramzan, M., et al. (2021). Gastrointestinal Tract Infections Classification Using Deep Learning. Computers, Materials & Continua, 69(3), 3239 – 3250.
Rashid, M., et al. (2019). Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools and Applications, 78(12), 15751-15777.
Raza, M., et al. (2018). Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Future Generation Computer Systems, 88, 28-39.
Raza, M., Sharif, M., Yasmin, M., Masood, S., & Mohsin, S. (2012). Brain image representation and rendering: A survey. Research Journal of Applied Sciences, Engineering and Technology, 4(18), 3274-3282.
Rehman, M., Iqbal, M., Sharif, M., & Raza, M. (2012). Content based image retrieval: survey. World Applied Sciences Journal, 19(3), 404-412.
Rehman, M., Sharif, M., & Raza, M. (2014). Image compression: A survey. Research Journal of Applied Sciences, Engineering and Technology, 7(4), 656-672.
Rodríguez-López, V., & Cruz-Barbosa, R. (2015, June). Improving bayesian networks breast mass diagnosis by using clinical data. In Mexican Conference on Pattern Recognition (pp. 292-301). Springer, Cham.
Rodriguez-Ruiz, A., et al. (2019). Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute, 111(9), 916-922.
Roselin, R., & Thangavel, K. (2012, March). Mammogram image segmentation using granular computing based on rough entropy. In International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) (pp. 318-323). IEEE.
Rouhi, R., Jafari, M., Kasaei, S., & Keshavarzian, P. (2015). Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications, 42(3), 990-1002.
Saba, T., et al. (2018). Fundus image classification methods for the detection of glaucoma: A review. Microscopy research and technique, 81(10), 1105-1121.
Saba, T., et al. (2021). Suspicious activity recognition using proposed deep L4-branched-ActionNet with entropy coded ant colony system optimization. IEEE Access, 9, 89181-89197.
Saha, M., Naskar, M. K., & Chatterji, B. N. (2015). Soft, hard and block thresholding techniques for denoising of mammogram images. IETE Journal of Research, 61(2), 186-191.
Saidin, N., et al. (2013). Computer aided detection of breast density and mass, and visualization of other breast anatomical regions on mammograms using graph cuts. Computational and mathematical methods in medicine, 2013. 205384.
Sakai, A., et al. (2020). A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features. Radiological Physics and Technology, 13(1), 27-36.
Sanuade, O. A., et al. (2021). Understanding the causes of breast cancer treatment delays at a teaching hospital in Ghana. Journal of health psychology, 26(3), 357-366.
Sarangi, S., Rath, N. P., & Sahoo, H. K. (2021). Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold. Medical & Biological Engineering & Computing, 59(4), 947-955.
Sasikala, S., Ezhilarasi, M., & Arun Kumar, S. (2020). Detection of breast cancer using fusion of MLO and CC view features through a hybrid technique based on binary firefly algorithm and optimum-path forest classifier. In Applied Nature-Inspired Computing: Algorithms and Case Studies (pp. 23-40). Springer, Singapore.
Scharcanski, J., & Jung, C. R. (2006). Denoising and enhancing digital mammographic images for visual screening. Computerized Medical Imaging and Graphics, 30(4), 243-254.
Setiawan, A. S., Wesley, J., & Purnama, Y. (2015). Mammogram classification using law’s texture energy measure and neural networks. Procedia Computer Science, 59, 92-97.
Shah, G. A., et al. (2015). A review on image contrast enhancement techniques using histogram equalization. Science International, 27(2).
Shah, N. N., Ratanpara, T. V., & Bhensdadia, C. K. (2014). Early breast cancer tumor detection on mammogram images. International Journal of Computer Applications, 87(14).
Shahzad, A., et al. (2021). Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. Complex & Intelligent Systems, 1-17.
Shahzad, A., Sharif, M., Raza, M., & Hussain, K. (2008). Enhanced watershed image processing segmentation. Journal of Information & Communication Technology, 2(1), 01-09.
Sharif, M. I., et al. (2021). A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems, 1-14.
Sharif, M. I., Li, J. P., Naz, J., & Rashid, I. (2020). A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters, 131, 30-37.
Sharif, M., Amin, J., Raza, M., Yasmin, M., & Satapathy, S. C. (2020). An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recognition Letters, 129, 150-157.
Sharif, M., et al. (2020). Brain tumor detection based on extreme learning. Neural Computing and Applications, 32(20), 15975-15987.
Sharif, M., Irum, I., Yasmin, M., & Raza, M. (2017). Salt & pepper noise removal from digital color images based on mathematical morphology and fuzzy decision. Nepal Journal of Science and Technology, 18(1), 1-7.
Sharif, M., Mohsin, S., Jamal, M. J., & Raza, M. (2010, July). Illumination normalization preprocessing for face recognition. In 2010 the 2nd conference on environmental science and information application technology (Vol. 2, pp. 44-47). IEEE.
Sharma, J., & Sharma, S. (2011). Mammogram image segmentation using watershed. Int J Info Tech and Knowledge Management, 4, 423-5.
Shinde, V., & Thirumala Rao, B. (2019). Novel approach to segment the pectoral muscle in the mammograms. In Cognitive Informatics and Soft Computing (pp. 227-237). Springer, Singapore.
Singh, G., & Mittal, A. (2014). Various image enhancement techniques-a critical review. International Journal of Innovation and Scientific Research, 10(2), 267-274.
Singh, V. K., et al. (2020). Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Systems with Applications, 139, 112855.
Souto, L. P. M., dos Santos, T. K., & Silva, M. P. S. (2018, July). Classification of breast tumors through image mining techniques. In Anais do XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. SBC.
Sreedevi, S., & Sherly, E. (2015). A novel approach for removal of pectoral muscles in digital mammogram. Procedia Computer Science, 46, 1724-1731.
Stephan, P. (2019). Breast Tumor Size and Staging. Available: https://www.verywellhealth.com/know-your-breast-tumor-size-4114640?print
Suri, J. S., Sun, Y., & Janer, R. (2019). U.S. Patent No. 10,363,010. Washington, DC: U.S. Patent and Trademark Office.
Taifi, K., et al. (2020). Mammogram classification using nonsubsampled contourlet transform and gray-level co-occurrence matrix. In Critical Approaches to Information Retrieval Research (pp. 239-255). IGI Global.
Tavakoli, N., et al. (2019). Detection of abnormalities in mammograms using deep features. Journal of Ambient Intelligence and Humanized Computing, 1-13.
Tsochatzidis, L., Costaridou, L., & Pratikakis, I. (2019). Deep learning for breast cancer diagnosis from mammograms—a comparative study. Journal of Imaging, 5(3), 37.
Uddin, K. S., & Zhu, Q. (2019). Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements. Journal of biomedical optics, 24(5), 056005.
Vyshnavi, V., Vijayan, D., & Lavanya, R. (2021, March). Breast Density Classification in Mammogram Images. In 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) (pp. 1-5). IEEE.
Wang, R., ert al. (2019). Multi-level nested pyramid network for mass segmentation in mammograms. Neurocomputing, 363, 313-320.
Yamamoto, S., et al. (2019). Effective treatment of a malignant breast phyllodes tumor with doxorubicin-ifosfamide therapy. Case Reports in Oncological Medicine, 2019.
Yasmin, M., et al. (2012). Brain image reconstruction: A short survey. World Applied Sciences Journal, 19(1), 52-62.
Yasmin, M., Mohsin, S., Sharif, M., Raza, M., & Masood, S. (2012). Brain image analysis: a survey. World Applied Sciences Journal, 19(10), 1484-1494.
Yasmin, M., Sharif, M., & Mohsin, S. (2013). Survey paper on diagnosis of breast cancer using image processing techniques. Res. J. Recent Sci. ISSN, 2277, 2502.
Yasmin, M., Sharif, M., Masood, S., Raza, M., & Mohsin, S. (2012). Brain image enhancement-A survey. World Applied Sciences Journal, 17(9), 1192-1204.
Yoon, W. B., et al. (2016). Automatic detection of pectoral muscle region for computer-aided diagnosis using MIAS mammograms. BioMed research international, 2016.
Yousefi, P. (2015, November). Mammographic image enhancement for breast cancer detection applying wavelet transform. In 2015 IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES) (pp. 82-86). IEEE.
Zebari, D. A., et al. (2020). Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. Ieee Access, 8, 203097-203116.
Zhou, K., Li, W., & Zhao, D. (2022). Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+. Technology and Health Care, (Preprint), 1-18.