Browsing by Author "Nilsson, Mikael"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Detection of vascular intersection in retina fundus image using modified cross point number and neural network technique(IEEE, 2008-05-13) Iqbal, Muhammad I.; Aibinu, A. M.; Nilsson, Mikael; Tijani, I. B.; Salami, Momoh-Jimoh E.Vascular intersection can be used as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images. In this work we apply the knowledge of digital image processing, fuzzy logic and neural network technique to detect bifurcation and vein-artery cross-over points in fundus images. The acquired images undergo preprocessing stage for illumination equalization and noise removal. Segmentation stage clusters the image into two distinct classes by the use of fuzzy c-means technique, neural network technique and modified cross-point number (MCN) methods were employed for the detection of bifurcation and cross-over points. MCN uses a 5x5 window with 16 neighboring pixels for efficient detection of bifurcation and cross over points in fundus images. Result obtained from applying this hybrid method on both real and simulated vascular points shows that this method perform better than the existing simple cross-point number (SCN) method, thus an improvement to the vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy.Item Vascular intersection detection in retina fundus images using a new hybrid approach(Pergamon, 2010-01-01) Aibinu, Abiodun M.; Iqbal, Muhammad I.; Shafie, Amir A.; Salami, Momoh-Jimoh E.; Nilsson, MikaelThe use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods