Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets - S. S. Tadasare; S. S. Pawar (International Journal of Advances in Applied Sciences, 2018)
In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.