Image Processing

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model

[+] Author Affiliations
Sinan Onal, Xin Chen

Southern Illinois University-Edwardsville, Department of Mechanical and Industrial Engineering, Box 1805, Edwardsville, Illinois 62026, United States

Veeresh Satamraju

Southern Illinois University-Edwardsville, Department of Electrical and Computer, Box 1801, Edwardsville, Illinois 62026, United States

Maduka Balasooriya

Southern Illinois University-Edwardsville, Department of Mathematics and Statistics, Box 1805, Edwardsville, Illinois 62026, United States

Humeyra Dabil-Karacal

Washington University, School of Medicine, Department of Ophthalmology and Visual Sciences, 10 Barnes West Drive Suite 201 C, St. Louis, Missouri 63141, United States

J. Med. Imag. 3(3), 034002 (Sep 12, 2016). doi:10.1117/1.JMI.3.3.034002
History: Received June 15, 2016; Accepted August 23, 2016
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Abstract.  Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Sinan Onal ; Xin Chen ; Veeresh Satamraju ; Maduka Balasooriya and Humeyra Dabil-Karacal
"Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model", J. Med. Imag. 3(3), 034002 (Sep 12, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.3.034002


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