Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector
Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmen...
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6380 |
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| author | Alexander Ze Hwan Ooi Zunaina Embong Aini Ismafairus Abd Hamid Rafidah Zainon Shir Li Wang Theam Foo Ng Rostam Affendi Hamzah Soo Siang Teoh Haidi Ibrahim |
| author_facet | Alexander Ze Hwan Ooi Zunaina Embong Aini Ismafairus Abd Hamid Rafidah Zainon Shir Li Wang Theam Foo Ng Rostam Affendi Hamzah Soo Siang Teoh Haidi Ibrahim |
| author_sort | Alexander Ze Hwan Ooi |
| collection | DOAJ |
| description | Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation. |
| first_indexed | 2024-03-10T06:51:45Z |
| format | Article |
| id | doaj.art-115dc4afd12d41ab9af175ad9ec7797f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:45Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-115dc4afd12d41ab9af175ad9ec7797f2023-11-22T16:45:12ZengMDPI AGSensors1424-82202021-09-012119638010.3390/s21196380Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge DetectorAlexander Ze Hwan Ooi0Zunaina Embong1Aini Ismafairus Abd Hamid2Rafidah Zainon3Shir Li Wang4Theam Foo Ng5Rostam Affendi Hamzah6Soo Siang Teoh7Haidi Ibrahim8School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, MalaysiaDepartment of Ophthalmology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, MalaysiaDepartment of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, MalaysiaOncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, SAINS@BERTAM, Kepala Batas 13200, Pulau Pinang, MalaysiaFaculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, MalaysiaCentre of Global Sustainability Studies (CGSS), Level 5, Hamzah Sendut Library, Universiti Sains Malaysia, USM, Minden 11800, Pulau Pinang, MalaysiaFakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, MalaysiaSchool of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, MalaysiaSchool of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, MalaysiaOptometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.https://www.mdpi.com/1424-8220/21/19/6380blood vesselsedge segmentationfundus imagesretinal |
| spellingShingle | Alexander Ze Hwan Ooi Zunaina Embong Aini Ismafairus Abd Hamid Rafidah Zainon Shir Li Wang Theam Foo Ng Rostam Affendi Hamzah Soo Siang Teoh Haidi Ibrahim Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector Sensors blood vessels edge segmentation fundus images retinal |
| title | Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector |
| title_full | Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector |
| title_fullStr | Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector |
| title_full_unstemmed | Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector |
| title_short | Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector |
| title_sort | interactive blood vessel segmentation from retinal fundus image based on canny edge detector |
| topic | blood vessels edge segmentation fundus images retinal |
| url | https://www.mdpi.com/1424-8220/21/19/6380 |
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