Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images
Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9249425/ |
| _version_ | 1818930647080632320 |
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| author | Dilovan Asaad Zebari Diyar Qader Zeebaree Adnan Mohsin Abdulazeez Habibollah Haron Haza Nuzly Abdull Hamed |
| author_facet | Dilovan Asaad Zebari Diyar Qader Zeebaree Adnan Mohsin Abdulazeez Habibollah Haron Haza Nuzly Abdull Hamed |
| author_sort | Dilovan Asaad Zebari |
| collection | DOAJ |
| description | Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification. |
| first_indexed | 2024-12-20T04:04:01Z |
| format | Article |
| id | doaj.art-d0b9929406514d13974efdc6b71c1ee4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:04:01Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj.art-d0b9929406514d13974efdc6b71c1ee42022-12-21T19:54:05ZengIEEEIEEE Access2169-35362020-01-01820309720311610.1109/ACCESS.2020.30360729249425Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram ImagesDilovan Asaad Zebari0https://orcid.org/0000-0002-7643-6359Diyar Qader Zeebaree1https://orcid.org/0000-0001-5272-8181Adnan Mohsin Abdulazeez2Habibollah Haron3https://orcid.org/0000-0002-3749-5364Haza Nuzly Abdull Hamed4https://orcid.org/0000-0001-8619-4149Center of Scientific Research and Development, Nawroz University, Duhok, IraqResearch Center, Duhok Polytechnic University, Duhok, IraqPresidency of Duhok Polytechnic University, Duhok, IraqFaculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaSegmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.https://ieeexplore.ieee.org/document/9249425/Breast cancerdigital mammogramthreshold techniqueML~techniquebreast segmentationpectoral muscle segmentation |
| spellingShingle | Dilovan Asaad Zebari Diyar Qader Zeebaree Adnan Mohsin Abdulazeez Habibollah Haron Haza Nuzly Abdull Hamed Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images IEEE Access Breast cancer digital mammogram threshold technique ML~technique breast segmentation pectoral muscle segmentation |
| title | Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images |
| title_full | Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images |
| title_fullStr | Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images |
| title_full_unstemmed | Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images |
| title_short | Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images |
| title_sort | improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images |
| topic | Breast cancer digital mammogram threshold technique ML~technique breast segmentation pectoral muscle segmentation |
| url | https://ieeexplore.ieee.org/document/9249425/ |
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