Background: Diagnosis of ophthalmologic and cardiovascular systems most often rely
on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in
the retinal fundus images can aid in the early screening or detection of many ophthalmological
diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic
nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation
using improved SOM (iSOM) and ANN classifier is presented.
Methods: Morphological operations are carried out to enhance the input image. Clustering of pixels
is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein
a new node is introduced and new learning methodology is adopted using constrained weight
updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and
Results: Segmentation is tested on public image sets, High Resolution Fundus (HRF) images and
DRIONS-DB databases for Accuracy, Recall rate, Precision, F-Score, AUC and JC. The results
achieve an appreciable level of accuracy (~97%) as compared to other similar methods of classification.
The average time taken is less in estimating the neuron class and is about 12.1 sec per image
when evaluated on Intel Core i5 CPU running at 2.30 GHz coupled with 4 GB RAM. The
mean squared error for the segmented images is found to be in the range of 4-5%.
Conclusion: Segmentation of retinal blood vessels based on artificial neural networks employing
iSOM preserves the topology consuming less time for constrained weight updation achieving better
results than SOM. A new model to detect vessels can be developed by concatenating iSOMs in
parallel for multi class functions.