Fast Watershed Segmentation for Breast Cancer Detection
Keywords:Mammogram, Geometric Features, Gradient Features, Texture Features, Keypoint detection, Watershed segmentation
Within engineering and computer specializations, image processing is the most important study topic. It is one of today's fastest-growing technologies, with applications in a variety of biological sectors, including cancer sickness. According to the latest data from throughout the world, breast cancer is the most lethal of all cancer kinds. It is the most frequent cancer in women and the second leading cause of cancer mortality in women. In this study, we advocate using a watershed transformation to create a fast segmentation technique. This allows for the blending of updated information about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique. The method requires a mechanism to express the test picture in terms of the amount of change around every given pixel before it can begin the watershed modification. Each pixel in the greyscale representation of the original picture is subjected to the Sobel operator. According to the form, the tumors identified are round or semicircular and the light of the tumor dims as we travel away from its core. The complement for this previous data may be seen as a local minimum that necessitated the start of the watershed process. As a result, each tumor picture may be represented as a lake, with the center in the complement tumor picture being the least value. The identification of tumor percentage gets more reliable after using the approach. As a consequence, our computer-aided diagnostic method for mammographic breast cancer detection has improved significantly thanks to the novel methodology. The method was written in MATLAB and tested on a Windows computer. The strategy was put to the test using photos from MIAS (Mammogram Image Analysis Society, UK), which offers a consistent categorization system for mammographic examinations. In this study, we advocate using a watershed transformation to create a rapid segmentation technique. This allows for the blending of data about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique.
S. Osama, H. M. Kelash, G. Mahrous, and O. S. F. Allah “A Novel CAD System for Breast Cancer Detection”, Cancer Biology, Vol. 4, No. 3, pp. 335-340, 2014.
S. Mohammad “Detection of soft tissue abnormalities in mammographic images for early diagnosis of breast cancer”, Diss. University of British Columbia, 1998.
S. J. Melvin, D. L. Michael, R. Abram “Image-detected breast cancer: state of the art diagnosis and treatment”, Journal of the American College of Surgeons, Vol. 201, No 4, pp. 586-597, 2001.
S. Homero, T. S. Vivian, F. A. Michele “Segmentation technique for detecting suspect masses in dense breast digitized images as a tool for mammography CAD schemes”, In : Proceedings of the 2008 ACM symposium on Applied computing, p. 1333-1337, 2008.
L. Scott, S. Gary, W. Kenneth “Development of mammogram computer-aided diagnosis systems using optical processing technology”, Proceedings 29th Applied Imagery Pattern Recognition Workshop, pp. 173-179, 2020.
E. Z. Ali “Feature extraction values for breast cancer mammography images”, International Conference on Bioinformatics and Biomedical Technology, pp. 335-340, 2010.
J. P. Suckling “The mammographic image analysis society digital mammogram database”, Digital Mammo, pp. 375-386, 1994.
S. Yanni, W. Yuanyuan “Computer-Aided Classification of Breast Tumors Using the Affinity Propagation Clustering”, International Conference on Bioinformatics and Biomedical Engineering, pp. 1-4, 2010.
C. R. Feng, W. W. Wen-Jie, M. W. Kyung, “Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors”, Breast cancer research and treatment, Vol. 89, No. 2, p. 179, 2009.
C. Yunmei, T. Sheshadri, T. D. Hemant “On the incorporation of shape priors into geometric active contours”, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, pp. 145-152, 2001.
V. D. Velden, A. P. Schouten, B. Carla, B. Peter “The value of magnetic resonance imaging in diagnosis and size assessment of in situ and small invasive breast carcinoma”, The American journal of surgery, Vol. 192, No. 2, pp. 172-178, 2006.
M. Tomoko, F. Hiroshi, K. Satoshi “Development of new schemes for detection and analysis of mammographic masses”, Proceedings Intelligent Information Systems, pp. 63-66, 1997.
M. Arianna, S. Marcello, L. Roberto “Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing”, IEEE transactions on instrumentation and measurement, Vol. 57, No. 7, pp. 1422-1430, 2008.
C. M. Victoria, P. Rayon “Circumscribed mass detection in digital mammograms”, Electronics, Robotics and Automotive Mechanics Conference. pp.19-24, 2006.
S. R. Osama, M. Alruily, M. Alsmarah and M. Alruwaill “Breast cancer detection using modified Hough transform”, Biomedical Research, Vol. 29, No. 16, pp. 3188-3191, 2008.
S. R. Osama and G. Attiya “Classification of Mammograms Tumors Using Fourier Analysis”, International Journal of Computer Science and Network Security, Vol. 14, No. 2, pp. 110-115, 2010.
N. S. Hari, M. Pragnyaban, K. S. Vani “Qualitative Metrics on Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 8, No. 2, pp. 259-264, 2019.
A. Jose, D. Sujitha “Recent advances and investigation of efficient Computer Aided Diagnosis systems for CT images in Liver cancer detection”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 8, No. 3, pp. 343- 348, 2019.
A. Anbarasi and K. C. Nithyasree. "COVID-19 Detection in CT Images using Deep Transfer Learning." International Transactions on Electrical Engineering and Computer Science, Vol.1 No. 1, pp. 1-7, 2020.
T. Swarna Latha, “Recognition of Blood Cancer Using Different Classification Techniques”, International Transactions on Electrical Engineering and Computer Science, Vol. 1, No. 33-41, pp. 33-41, 2020.
How to Cite
Copyright (c) 2023 Sk. Nazma Sultana, U Janardhan Reddy, V Nagi Reddy
This work is licensed under a Creative Commons Attribution 4.0 International License.