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| author_facet |
Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid |
|---|---|
| author |
Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid |
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Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid Sensors A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
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khawaja, ahsan |
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Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s19224949 <jats:p>The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.</jats:p> A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation Sensors |
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10.3390/s19224949 |
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Physik Chemie und Pharmazie Allgemeines Technik Mathematik |
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| title |
A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
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A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
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A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
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A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
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A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_short |
A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_sort |
a multi-scale directional line detector for retinal vessel segmentation |
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Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
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http://dx.doi.org/10.3390/s19224949 |
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2019 |
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<jats:p>The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.</jats:p> |
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| author | Khawaja, Ahsan, Khan, Tariq M., Khan, Mohammad A. U., Nawaz, Syed Junaid |
| author_facet | Khawaja, Ahsan, Khan, Tariq M., Khan, Mohammad A. U., Nawaz, Syed Junaid, Khawaja, Ahsan, Khan, Tariq M., Khan, Mohammad A. U., Nawaz, Syed Junaid |
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| description | <jats:p>The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.</jats:p> |
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| spelling | Khawaja, Ahsan Khan, Tariq M. Khan, Mohammad A. U. Nawaz, Syed Junaid 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s19224949 <jats:p>The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.</jats:p> A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation Sensors |
| spellingShingle | Khawaja, Ahsan, Khan, Tariq M., Khan, Mohammad A. U., Nawaz, Syed Junaid, Sensors, A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation, Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| title | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_full | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_fullStr | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_full_unstemmed | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_short | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| title_sort | a multi-scale directional line detector for retinal vessel segmentation |
| title_unstemmed | A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation |
| topic | Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| url | http://dx.doi.org/10.3390/s19224949 |