Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features
Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the dev...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2012-05-01
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| Series: | Iraqi Journal of Physics |
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| Online Access: | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/769 |
| _version_ | 1797871309222313984 |
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| author | Faleh H. Mahmood |
| author_facet | Faleh H. Mahmood |
| author_sort | Faleh H. Mahmood |
| collection | DOAJ |
| description |
Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two stages performed using the minimum distance (MD) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLCM for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from Al-Ilwiya Hospital in Baghdad, Iraq. The proposed technique shows better results
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| first_indexed | 2024-04-10T00:41:55Z |
| format | Article |
| id | doaj.art-8039999bea7649d8aa913991588eded4 |
| institution | Directory Open Access Journal |
| issn | 2070-4003 2664-5548 |
| language | English |
| last_indexed | 2024-04-10T00:41:55Z |
| publishDate | 2012-05-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Iraqi Journal of Physics |
| spelling | doaj.art-8039999bea7649d8aa913991588eded42023-03-14T05:45:59ZengUniversity of BaghdadIraqi Journal of Physics2070-40032664-55482012-05-011017Early detection of breast cancer mass lesions by mammogram segmentation images based on texture featuresFaleh H. Mahmood0Remote Sensing Unit, College of Science, Baghdad University, Baghdad, Iraq Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years. This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two stages performed using the minimum distance (MD) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLCM for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from Al-Ilwiya Hospital in Baghdad, Iraq. The proposed technique shows better results https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/769Breast Cancer, Mammogram, Masses, calcifications, segmentation, Co-occurrence matrices |
| spellingShingle | Faleh H. Mahmood Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features Iraqi Journal of Physics Breast Cancer, Mammogram, Masses, calcifications, segmentation, Co-occurrence matrices |
| title | Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| title_full | Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| title_fullStr | Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| title_full_unstemmed | Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| title_short | Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| title_sort | early detection of breast cancer mass lesions by mammogram segmentation images based on texture features |
| topic | Breast Cancer, Mammogram, Masses, calcifications, segmentation, Co-occurrence matrices |
| url | https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/769 |
| work_keys_str_mv | AT falehhmahmood earlydetectionofbreastcancermasslesionsbymammogramsegmentationimagesbasedontexturefeatures |