Yogesh M. Rajput

Personal Identification Algorithm Based on Retinal Blood Vessels Bifurcation

Biometric identifiers are the unique, measurable characteristics used to tag and describe individuals. Physiological characteristics are related to the shape of the body. Examples of biometric identifications are, fingerprint, face, DNA, Palm print, hand geometry, iris recognition, and retina. Human retina is another source of biometric system which provides the most reliable and stable means of authentication. We propose a new algorithm for the detection and measurement of blood vessels of the retina and finding the bifurcation points of blood vessels for personal identification. A minutiae technique for finding bifurcation points of the extracted blood vessels and according to bifurcation points identifies the individual person. Performance of these techniques is tested using the database from Dr. Manoj Saswade and Dr. Neha Deshpande (300 Images). This algorithm achieves a true positive rate of 98%, false positive rate of 20%, and accuracy score of 0.9702 and also classification down through Statistical Techniques.

Detection of non-proliferative diabetic retinopathy lesions using wavelet and classification using K-means clustering

WHO predicts that in year 2012 there are about 347 million people worldwide have diabetes, more than 80% of diabetes deaths occur in different countries. WHO projects that diabetes will be the 7th major cause leading death in 2030. Diabetic Retinopathy caused by leakage of blood or fluid from the retinal blood vessels and it will damage the retina. Non-proliferative diabetic retinopathy (NPDR) is an early stage of diabetic retinopathy and it is categorized into three stages they are mild, moderate and sever NPDR. The characteristic of the Mild; is specified by the presence of minimum microaneurysm, Moderate; specifies the presence of hemorrhages, microaneurysms, and hard exudates where as Severe; determine on the blockage of vessels, depriving several areas of the retina. With their blood supply. These areas of the retina send signals to the body to grow new blood vessels for nourishment. The proposed algorithm tested on online databases like STARE, DRIVE, DiarectDB0, DiarectDB1 and SASWADE (the database collected during the research work). The statistical techniques were applied on NPDR lesion and calculate the mean, variance, standard deviation, & correlation for classification. K-means clustering have been applied on the dataset with extracted features 95% of correct classification have been achieved.

Secondary glaucoma diagnosis technique using retinal nerve fiber layer arteries

Glaucoma is an eye disease. In glaucoma retinal nerve fiber layers are damaged and if it is not treated earlier then it can cause permanent vision loss. This paper represents algorithm for detection of glaucoma using retinal nerve fiber layers. For this work we have used 2D median filter and HAAR wavelet transform methods. For this work we have also used Drishti-GS dataset which contains 101 glaucomatous images and HRF (High Resolution Fundus image) database. We have extracted the retinal nerve fiber layer Arteries. Then we have calculated its area and diameter. On the normal database we got the 100% result. We got 71.28% accuracy on glaucomatous images and when we have combined the normal and glaucomatous images then we got the 62.06% accuracy.

Use of Quality Measures for Rural Indian Fingerprint Image Database Enhancement and Improve the Recognition Rate

Identification and authentication is done using various biometric sign like fingerprints. The recognition rate of correct person is depending on quality of fingerprints images. Fingerprints quality also varying from rural and urban population. Rural population having more physical work than urban population. Therefore the ridges, valleys, bifurcation, joints, minutia etc. features are not good quality hence it reduces recognition rate accuracy. To improve recognition rate of such images there is strong need to first improve the quality of features. In this paper used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1632 fingerprints images. Out of which preprocess 100 sample images using histogram equalization and tried to improve the quality of images. The resultant images quality is verified by using different quality measures like PSNR, MSE, MAXERR, L2RAT, it is found that quality has been improved. Hence it is proved that the recognition rate is increases.

Design New Biorthogonal Wavelet Filter for Extraction of Blood Vessels and Calculate the Statistical Features

World health organization predicts that in year 2012 there are about 347 million people worldwide have diabetes, more than 80 % of diabetes deaths occur in different countries. WHO projects that diabetes will be the 7th major cause leading death in 2030. Diabetic Retinopathy caused by leakage of blood or fluid from the retinal blood vessels and it will damage the retina. For extraction of retinal blood vessels we have invent new wavelet filter. The proposed filter gives the good extraction result as compare to exiting wavelet filter. In proposed algorithm, we have extract the retinal blood vessels features like area, diameter, length, thickness, mean, tortuosity, and bifurcations. The proposed algorithm is tested on 1191 fundus images and achieves sensitivity of 98 %, specificity of 92 % and accuracy of 95 %.