Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening

作者：GeethaRamani R; Balasubramanian Lakshmi^*

来源：Computer Methods and Programs in Biomedicine, 2018, 160: 153-163.

DOI：10.1016/j.cmpb.2018.03.020

• Fundus image
• Macula and fovea
• Clustering
• Image processing
• Data mining

Background and objective: Macula segmentation and fovea localization is one of the primary tasks in retinal analysis as they are responsible for detailed vision. Existing approaches required segmentation of retinal structures viz. optic disc and blood vessels for this purpose.
Method: This work avoids knowledge of other retinal structures and attempts data mining techniques to segment macula. Unsupervised clustering algorithm is exploited for this purpose. Selection of initial cluster centres has a great impact on performance of clustering algorithms. A heuristic based clustering in which initial centres are selected based on measures defining statistical distribution of data is incorporated in the proposed methodology. The initial phase of proposed framework includes image cropping, green channel extraction, contrast enhancement and application of mathematical closing. Then, the preprocessed image is subjected to heuristic based clustering yielding a binary map. The binary image is post-processed to eliminate unwanted components. Finally, the component which possessed the minimum intensity is finalized as macula and its centre constitutes the fovea.
Results: The proposed approach outperforms existing works by reporting that 100%, of HRF, 100% of DRIVE, 96.92% of DIARETDB0, 97.75% of DIARETDB1, 98.81% of HEI-MED, 90% of STARE and 99.33% of MESSIDOR images satisfy the 1R criterion, a standard adopted for evaluating performance of macula and fovea identification.
Conclusion: The proposed system thus helps the ophthalmologists in identifying the macula thereby facilitating to identify if any abnormality is present within the macula region.

• 出版日期2018-7