Mammogram segmentation using multi-atlas deformable registration
| Autor: | Anup K. Sadhu, Mainak Jas, Vikrant Anilrao Karale, Manish Kumar Sharma, Sudipta Mukhopadhyay |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Jaccard index Databases Factual Image quality Computer science education ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Health Informatics Pattern Recognition Automated 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted Humans Segmentation Breast Cluster analysis Projection (set theory) Standard test image Atlas (topology) business.industry Model selection Multi atlas Pattern recognition Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Hausdorff distance Female Artificial intelligence business Algorithms 030217 neurology & neurosurgery Mammography |
| Zdroj: | Computers in Biology and Medicine. 110:244-253 |
| ISSN: | 0010-4825 |
| DOI: | 10.1016/j.compbiomed.2019.06.001 |
| Popis: | Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. Existing segmentation algorithms underperform due to variations in image quality and shape of the breast region. In this paper, we propose to segment breast region by combining data-driven clustering with deformable image registration. In the first phase of the approach, we identify atlas images from a dataset of mammograms using data-driven clustering. Then, we segment these atlas images and use in the next phase of the algorithm. The second phase is atlas-based registration. For a candidate image, we find the most similar atlas image from the set of atlases identified in phase one. We deform the selected atlas image to match the given test image using the Demon's registration algorithm. Then, the segmentation mask of the deformed atlas is transferred to the mammogram in consideration. Finally, we refine the segmentation mask with some morphological operations in order to obtain accurate breast region boundary. We evaluated the performance of our method using ground-truth segmentation masks verified by an expert radiologist. We compared the proposed method with three existing state-of-the-art algorithms for breast region segmentation and the proposed approach outperformed all three in most of the cases. |
| Databáze: | OpenAIRE |
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