Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector
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
Affiliations
Alexander Ze Hwan Ooi
School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Zunaina Embong
Department of Ophthalmology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
Aini Ismafairus Abd Hamid
Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
Rafidah Zainon
Oncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, SAINS@BERTAM, Kepala Batas 13200, Pulau Pinang, Malaysia
Shir Li Wang
Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia
Theam Foo Ng
Centre of Global Sustainability Studies (CGSS), Level 5, Hamzah Sendut Library, Universiti Sains Malaysia, USM, Minden 11800, Pulau Pinang, Malaysia
Rostam Affendi Hamzah
Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia
Soo Siang Teoh
School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Haidi Ibrahim
School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
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.
