An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images
Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presenc...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Politeknik Elektronika Negeri Surabaya
2022-06-01
|
| Series: | Emitter: International Journal of Engineering Technology |
| Subjects: | |
| Online Access: | https://emitter.pens.ac.id/index.php/emitter/article/view/695 |
| _version_ | 1818214151323910144 |
|---|---|
| author | Nada Fitrieyatul Hikmah Tri Arief Sardjono Windy Deftia Mertiana Nabila Puspita Firdi Diana Purwitasari |
| author_facet | Nada Fitrieyatul Hikmah Tri Arief Sardjono Windy Deftia Mertiana Nabila Puspita Firdi Diana Purwitasari |
| author_sort | Nada Fitrieyatul Hikmah |
| collection | DOAJ |
| description |
Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
|
| first_indexed | 2024-12-12T06:15:37Z |
| format | Article |
| id | doaj.art-51765c68b1ce4b7abeb78ad809288090 |
| institution | Directory Open Access Journal |
| issn | 2355-391X 2443-1168 |
| language | English |
| last_indexed | 2024-12-12T06:15:37Z |
| publishDate | 2022-06-01 |
| publisher | Politeknik Elektronika Negeri Surabaya |
| record_format | Article |
| series | Emitter: International Journal of Engineering Technology |
| spelling | doaj.art-51765c68b1ce4b7abeb78ad8092880902022-12-22T00:35:02ZengPoliteknik Elektronika Negeri SurabayaEmitter: International Journal of Engineering Technology2355-391X2443-11682022-06-0110110.24003/emitter.v10i1.695An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic ImagesNada Fitrieyatul Hikmah0Tri Arief Sardjono1Windy Deftia Mertiana2Nabila Puspita Firdi3Diana Purwitasari4Institut Teknologi Sepuluh NopemberDepartment of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, IndonesiaDepartment of Informatics Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment. https://emitter.pens.ac.id/index.php/emitter/article/view/695Breast CancerCC viewEntropyFeature ExtractionMammographyMLO view |
| spellingShingle | Nada Fitrieyatul Hikmah Tri Arief Sardjono Windy Deftia Mertiana Nabila Puspita Firdi Diana Purwitasari An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images Emitter: International Journal of Engineering Technology Breast Cancer CC view Entropy Feature Extraction Mammography MLO view |
| title | An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images |
| title_full | An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images |
| title_fullStr | An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images |
| title_full_unstemmed | An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images |
| title_short | An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images |
| title_sort | image processing framework for breast cancer detection using multi view mammographic images |
| topic | Breast Cancer CC view Entropy Feature Extraction Mammography MLO view |
| url | https://emitter.pens.ac.id/index.php/emitter/article/view/695 |
| work_keys_str_mv | AT nadafitrieyatulhikmah animageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT triariefsardjono animageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT windydeftiamertiana animageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT nabilapuspitafirdi animageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT dianapurwitasari animageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT nadafitrieyatulhikmah imageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT triariefsardjono imageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT windydeftiamertiana imageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT nabilapuspitafirdi imageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages AT dianapurwitasari imageprocessingframeworkforbreastcancerdetectionusingmultiviewmammographicimages |