Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection
DC Field | Value | Language |
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dc.contributor.author | Niaz, Asim | - |
dc.contributor.author | Memon, Asif Aziz | - |
dc.contributor.author | Rana, Kaynat | - |
dc.contributor.author | Joshi, Aditi | - |
dc.contributor.author | Soomro, Shafiullah | - |
dc.contributor.author | Kang, Jin Seok | - |
dc.contributor.author | Choi, Kwang Nam | - |
dc.date.accessioned | 2021-07-28T02:40:14Z | - |
dc.date.available | 2021-07-28T02:40:14Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47955 | - |
dc.description.abstract | The most fatal and frequent cancer amongst women is breast cancer. Mammography provides timely detection of lumps and masses in breast tissue, but effective diagnosis requires accurately identifying malignant tumor boundaries, which remains challenging, particularly for images with inhomogeneous regions. Therefore, we propose an active contour method based on a reformed combined local and global fitted function to address breast tumor segmentation. This combined function is strengthened by a proposed average energy driving function to capture obscure boundaries for regions of interest more precisely from inhomogeneous images. Including a p-Laplace term eliminates reinitialization requirements and suppresses false contours in the segmentation. Bias field signal, which causes image homogeneity corruption, is estimated by bias field initialization to ensure independence from the initial contour position. Local and global fitted models are incorporated by introducing bias fields within them. The proposed method was tested on the MIAS MiniMammographic Database, with quantitative analysis to calculate its accuracy, effectiveness, and efficiency. Experimentation confirmed the proposed method provided superior results compared with previous state-of-the-art methods. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3029333 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp 186851 - 186861 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000583565000001 | - |
dc.identifier.scopusid | 2-s2.0-85102809850 | - |
dc.citation.endPage | 186861 | - |
dc.citation.startPage | 186851 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Nonhomogeneous media | - |
dc.subject.keywordAuthor | Level set | - |
dc.subject.keywordAuthor | Mammography | - |
dc.subject.keywordAuthor | Active contours | - |
dc.subject.keywordAuthor | Cancer | - |
dc.subject.keywordAuthor | Breast | - |
dc.subject.keywordAuthor | Active contours | - |
dc.subject.keywordAuthor | bias field | - |
dc.subject.keywordAuthor | image segmentation | - |
dc.subject.keywordAuthor | intensity inhomogeneity | - |
dc.subject.keywordAuthor | level set | - |
dc.subject.keywordPlus | LEVEL SET EVOLUTION | - |
dc.subject.keywordPlus | REGION | - |
dc.subject.keywordPlus | BOUNDARY | - |
dc.subject.keywordPlus | MUMFORD | - |
dc.subject.keywordPlus | DRIVEN | - |
dc.subject.keywordPlus | ENERGY | - |
dc.subject.keywordPlus | FORCE | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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