Sijie Niu1,2, Qiang Chen2,3, Luis de Sisternes4, Theodore Leng5, Daniel L Rubin4. 1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China. 2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. 3. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China. 4. Department of Radiology, Stanford University, Stanford, CA, 94305, USA. 5. Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, 94303, USA.
Abstract
PURPOSE: To develop an automated method based on saliency map of the retinal thickness map to determine foveal center in spectral-domain optical coherence tomography (SD-OCT) images. METHODS: This paper proposes an automatic method for the detection of the foveal center in SD-OCT images. Initially, a retinal thickness map is generated by considering the axial distance between the internal limiting membrane (ILM) and the Bruch's membrane (BM). Both the ILM and BM boundaries are automatically segmented by a known retinal segmentation technique. The macular foveal region is identified as a salient feature in the retinal thickness map, and segmented by the saliency detection method based on a human vision attention model. Finally, the foveal center is identified by searching for the lowest point from the determined macular fovea region. RESULTS: Experimental results in 39 scans from 35 healthy eyes and 58 scans from 29 eyes diagnosed with several stages of age-related macular degeneration (AMD), from mild or intermediate stages to severe dry or wet stages, demonstrated that the proposed method achieves good performance. The mean radial distance error of the automatically detected foveal center locations when compared to consensus manual determination established by repeated sessions from two expert readers was 52 ± 56 μm for the normal eyes and 73 ± 63 μm for AMD eyes. CONCLUSIONS: The proposed algorithm was more effective for detecting the foveal center automatically in SD-OCT images than the state-of-art methods.
PURPOSE: To develop an automated method based on saliency map of the retinal thickness map to determine foveal center in spectral-domain optical coherence tomography (SD-OCT) images. METHODS: This paper proposes an automatic method for the detection of the foveal center in SD-OCT images. Initially, a retinal thickness map is generated by considering the axial distance between the internal limiting membrane (ILM) and the Bruch's membrane (BM). Both the ILM and BM boundaries are automatically segmented by a known retinal segmentation technique. The macular foveal region is identified as a salient feature in the retinal thickness map, and segmented by the saliency detection method based on a human vision attention model. Finally, the foveal center is identified by searching for the lowest point from the determined macular fovea region. RESULTS: Experimental results in 39 scans from 35 healthy eyes and 58 scans from 29 eyes diagnosed with several stages of age-related macular degeneration (AMD), from mild or intermediate stages to severe dry or wet stages, demonstrated that the proposed method achieves good performance. The mean radial distance error of the automatically detected foveal center locations when compared to consensus manual determination established by repeated sessions from two expert readers was 52 ± 56 μm for the normal eyes and 73 ± 63 μm for AMD eyes. CONCLUSIONS: The proposed algorithm was more effective for detecting the foveal center automatically in SD-OCT images than the state-of-art methods.