LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS
Many biomedical applications require detection of curvilinear networks in images, and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here we discuss a contrast independent approach to identify curvilinear structures based on oriented phase congruenc...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2012
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| _version_ | 1797106412117032960 |
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| author | Obara, B Fricker, M Grau, V IEEE |
| author_facet | Obara, B Fricker, M Grau, V IEEE |
| author_sort | Obara, B |
| collection | OXFORD |
| description | Many biomedical applications require detection of curvilinear networks in images, and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here we discuss a contrast independent approach to identify curvilinear structures based on oriented phase congruency, the Phase Congruency Tensor. We show that the proposed approach is largely insensitive to intensity variations along the curve, and provides successful detection within noisy regions. Moreover, we demonstrate that the proposed approach may be used in a wide range of curvilinear and non-curvilinear feature enhancement and detection methods, particularly where tensor representation of the image is explored. The performance of the Phase Congruency Tensor-based methods is evaluated by comparing it with state-of-the-art intensity-based methods on both synthetic and real images of biomedical networks. © 2012 IEEE. |
| first_indexed | 2024-03-07T07:02:04Z |
| format | Journal article |
| id | oxford-uuid:ffd6ddbc-da4a-484a-bd71-968ffe85c236 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:02:04Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:ffd6ddbc-da4a-484a-bd71-968ffe85c2362022-03-27T13:47:58ZLOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKSJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ffd6ddbc-da4a-484a-bd71-968ffe85c236EnglishSymplectic Elements at Oxford2012Obara, BFricker, MGrau, VIEEEMany biomedical applications require detection of curvilinear networks in images, and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here we discuss a contrast independent approach to identify curvilinear structures based on oriented phase congruency, the Phase Congruency Tensor. We show that the proposed approach is largely insensitive to intensity variations along the curve, and provides successful detection within noisy regions. Moreover, we demonstrate that the proposed approach may be used in a wide range of curvilinear and non-curvilinear feature enhancement and detection methods, particularly where tensor representation of the image is explored. The performance of the Phase Congruency Tensor-based methods is evaluated by comparing it with state-of-the-art intensity-based methods on both synthetic and real images of biomedical networks. © 2012 IEEE. |
| spellingShingle | Obara, B Fricker, M Grau, V IEEE LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title | LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title_full | LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title_fullStr | LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title_full_unstemmed | LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title_short | LOCAL PHASE APPROACHES TO EXTRACT BIOMEDICAL NETWORKS |
| title_sort | local phase approaches to extract biomedical networks |
| work_keys_str_mv | AT obarab localphaseapproachestoextractbiomedicalnetworks AT frickerm localphaseapproachestoextractbiomedicalnetworks AT grauv localphaseapproachestoextractbiomedicalnetworks AT ieee localphaseapproachestoextractbiomedicalnetworks |