Browsing by Author "Nilsson, M."
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Item Automatic diagnosis of diabetic retinopathy from fundus images using digital signal and image processing techniques(International Conference on Robotics, Vision, Information, and Signal Processing, 2011-11-28) Aibinu, A. M.; Iqbal, Muhammad I.; Nilsson, M.; Salami, Momoh-Jimoh E.Automatic diagnosis and display of diabetic retinopathy from images of retina using the techniques of digital signal and image processing is presented in this paper. The acquired images undergo pre-processing to equalize uneven illumination associated with the acquired fundus images. This stage also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the abnormalities detection stage was used to distinguish between candidate lesions and other information. Methods of diagnosis of red spots, bleeding and detection of vein-artery crossover points have also been developed in this work using the color information, shape, size, object length to breadth ration as contained in the acquired digital fundus image. The algorithm was tested with a separate set of 25 fundus images. From this, the result obtained for Microaneurysms and Haemorrhages diagnosis shows the appropriateness of the method.Item A new method of correcting uneven illumination problem in fundus image(2007) Aibinu, Abiodun M.; Iqbal, Muhammad I.; Nilsson, M.; Salami, Momoh-Jimoh E.Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction.