Segmentation of Cancerous Mammography using MATLAB
Abstract
Breast cancer is one of the main causes of cancer death in women. Detection is efficiently performed by using digital mammograms. Small clusters of micro calcifications appearing as a collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improving breast cancer diagnosis. To increase radiologists' diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary identification of this disease. In this research, an attempt is made to develop an adaptive K-means clustering algorithm for breast image segmentation to detect microcalcifications. The method was tested over several images of image databases taken from Mammocare, Ghana for cancer research and diagnosis. The algorithm works faster so that any radiologist can take a clear decision about the appearance of microcalcifications by visual inspection of digital mammograms and detection accuracy has also improved as compared to some existing works.
Downloads
How to Cite
References
M Abdel-Mottaleb, C Carman, S Hill, C, R Vafai, S (1996) Locating the Boundarybetween the Breast Skin Edge and the Background in Digitized Mammograms. 467-470.
A Adam, R Omar (2006) Computerized breast cancer diagnosis with Genetic Algorithm and Neural Network. 533-538.
N Basheer, M Mohammed (1998) Detection of Breast Cancer using Digital Image Processing Techniques. 8(2S2), 5-9.
P Bethapudi (1998) Malignant masses in mammograms for detection of breast cancer digital mammography. 15.
Ulrich Bick, Maryellen Giger, Robert Schmidt, Robert Nishikawa, Dulcy Wolverton, Kunio Doi (1997) Automated segmentation of digitized mammograms. 2(1), 1-9.
Dalmiya (2012) Learning Techniques for Mammogram Classification using wavelet and soft. 1(1), 50-96.
G Eklund, G Cardenosa, W Parsons (2001) Assessing adequacy of mammographic image quality.. 190(2), 297-307.
Maryellen Giger, L Nishikawa, R, M Kupinski, M (1997) Development of Methods for Computer-Assisted Interpretation of Digital Mammograms for Early Breast Cancer Detection.
Aboul Hassanien, Sameh Basha, Areeg Abdalla (2003) Generalization of Fuzzy C-Means based on Neutrosophic Logic. 27(1), 21-31.
T Kanungo, D Mount, N Netanyahu, C Piatko, R Silverman, A Wu (2002) An efficient k-means clustering algorithm: analysis and implementation. 24(7), 881-892.
S Lou, H Lin, K Lin, D Hoogstrate (2000) Automatic breast region extraction from digital mammograms for PACS and telemammography applications. 24(4), 205-220.
P Mary, S Vennila, I (2010) Optimization Fusion Approach for Image Segmentation Using K-Means Algorithm. 2(7).
H Mirzaalian, M Ahmadzadeh, R Jafari, M Preprocessing Algorithms on Digital Mammograms. 118-121.
A Mendez, J Tahoces, P, J Lado, M, J Souto, M Correa, J, L Vidal, J, J (1996) Automatic Detection of Breast Border and Nipple in Digital Mammograms. 49, 253-262.
T Ojala, J Näppi, O Nevalainen (2001) Accurate Segmentation of the Breast Region from Digitized Mammograms. 25(1), 47-59.
N Petrick, Heang-Ping Chan, B Sahiner, Datong Wei (1996) An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection. 15(1), 59-67.
Serhat Özekes (2005) A new method for automated mass detection in digital mammographic images using templates. 1, 12.
K Thangavel, M Karnan, R Sivakumar (2002) Automatic Detection of Microcalcification in Mammograms -A Review. 5(5), 23-53.
Michael Wirth, Alexei Stapinski (1995) Segmentation of the breast region in mammograms using active contours. 5150, 1995-2006.
C Vyborny, J Yin, F, F Giger, M, L Doi, K Metz, C, E Schmidt, R, A (1991) Computerized Detection of Masses in Digital Mammograms: Analysis of Bilateral Subtraction Images. 18(5), 955-963.
Published
2022-02-16
Issue
Section
License
Copyright (c) 2022 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.