Mr. Kedir M. Adal Profile

Kedir M. Adal

PhD Student at Technische Univ Delft

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Publications (3)

Proceedings Article | 3 March 2017 Paper

Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

Kedir Adal, Peter van Etten, Jose Martinez, Kenneth Rouwen, Koenraad Vermeer, Lucas van Vliet

Proceedings Volume 10134, 101341N (2017) https://doi.org/10.1117/12.2254342

KEYWORDS: Convolutional neural networks, Blood vessels, Neural networks, Eye, Eye models, Feature extraction, Retina, Machine learning, Data modeling, Image registration, Image analysis, RGB color model

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Proceedings Article | 18 March 2013 Paper

Automated detection of microaneurysms using robust blob descriptors

K. Adal, S. Ali, D. Sidibé, T. Karnowski, E. Chaum, F. Mériaudeau

Proceedings Volume 8670, 86700N (2013) https://doi.org/10.1117/12.2007913

KEYWORDS: Image quality, Image enhancement, Radon transform, Image contrast enhancement, Feature extraction, Image analysis, Computer aided diagnosis and therapy, Databases, Digital photography, Radon

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Proceedings Article | 13 March 2013 Paper

Steerable wavelet transform for atlas based retinal lesion segmentation

Sharib Ali, Kedir Adal, Désiré Sidibé, Edward Chaum, Thomas Karnowski, Fabrice Mériaudeau

Proceedings Volume 8669, 86693D (2013) https://doi.org/10.1117/12.2006357

KEYWORDS: Image segmentation, Eye, Macula, Gold, Optic nerve, Retina, Image processing, Image enhancement, Wavelet transforms, Principal component analysis

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Diabetic macular edema (DME) characterized by discrete white{yellow lipid deposits due to vascular leakage is one of the most severe complication seen in diabetic patients that cause vision loss in affected areas. Such vascular leakage can be treated by laser surgery. A regular follow{up and laser photocoagulation can reduce the risk of blindness by 90%. In an automated retina screening system, it is thus very crucial to make the segmentation of such hard exudates accurate and register these images taken over time to a reference co-ordinate system to make the necessary follow-ups more precise. We introduce a novel method of ethnicity based statistical atlas for exudates segmentation and follow-up. Ethnic background plays a significant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images. Such statistical atlas can thus help to provide a solution, simplify the image processing steps and increase the detection rate. In this paper, bright lesion segmentation is investigated and experimentally verified for the gold standard built from African American fundus images. 40 automatically generated landmark points on the major vessel arches with macula and optic centers are used to warp the retinal images. PCA is used to obtain a mean shape of the retinal major arches (both lower and upper). The mean of the co-ordinates of the macula and optic disk center are obtained resulting 42 landmark points and together they provide a reference co-ordinate frame ( or the atlas co-ordinate frame) for the images. The retinal funds images of an ethnic group without any artifact or lesion are warped to this reference co-ordinate frame from which we obtain a mean image representing the statistical measure of the chromatic distribution of the pigments in the eye of that particular ethnic group. 400 images of African American eye has been used to build such a gold standard for this ethnic group. Any test image of the patient of that ethnic group is first warped to the reference frame and then a distance map is obtained with this mean image. Finally, the post-processing schemes are applied on the distance map image to enhance the edges of the exudates. A multi-scale and multi-directional steerable filters along with the Kirsch edge detector was found to be promising. Experiments with the publicly available HEI-MED dataset showed the good performance of the proposed method. We achieved the lesion localization fraction (LLF) of 82.5% at 35% of non{lesion localization fraction (NLF) on the FROC curve.

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