Department of Computer Science and Information Systems
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Item Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters(Elsevier, 2022-01) Raman, SundaresanProblems and diseases with eye are common in diabetic patients. Early diagnosis and detection of various diseases like retinopathy, neuropathy and nephropathy is crucial in diabetic patients. Certain demographic and diagnostic parameters play a significant role in predicting diseases related to diabetes. Development of a novel diagnostic method which helps to predict the disease by establishing a significant correlation with the demographic and diagnostic parameters is of prime importance. This study proposes a new methodology in which retinal fractals are obtained for the images and the derived retinal fractals are analysed to aid in disease prediction. This study comprises of images from patients with retinopathy, non retinopathy, neuropathy, nephropathy and hypertension. The proposed research is carried out in two aspects: 1) to correlate the retinal fractals of retinopathy and non retinopathy images with certain demographic and diagnostic parameters and interpret its significance, and 2) to exhibit a relationship between the retinal fractals and various diseases/addictive habit to facilitate the prediction of the disease/addictive habit. Hausdorff fractal dimension (HFD) was applied and higher fractal dimension was obtained for healthy cases. Then using Statistical Package for the Social Sciences (SPSS) various statistical parameters and significance were calculated to analyse the relationship. Analysis results showed that fractal value helped in distinguishing between the retinopathy and non retinopathy conditions. It also helped in diagnosing the presence and absence of hypertension. Correlation analysis between certain demographic parameters and fractal value showed a positive correlation whereas few exhibited negative correlation.Item Comparison of various fractal analysis methods for retinal images(Elsevier, 2021-01) Raman, SundaresanRetinal vessels are known to behave like a fractal, waherein a part of a geometrical pattern resembles the whole. Although the box counting method has been used most commonly, currently there exists no "best method" for fractal analysis on retinal vessels. In the present study we compared the different methods of fractal analysis of retinal images. This study included 43 normal retinal images from public databases (STARE & DRIVE) and 40 retinal images (20 normal and 20 diseased) collected from an epidemiological study database (Sankara Nethralaya diabetic retinopathy epidemiology and molecular genetics study; SNDREAMS). In our study we calculated and compared the values of fractal dimensions by Box counting method, Hausdorff Fractal Dimension (HFD), Modified Hausdorff Fractal Dimension (MHFD) and Fourier Fractal Dimension (FFD). The coefficient of variation(CV) was the least with HFD methods in different databases (DRIVE & STARE: −0.088, SNDREAMS Normal retinal images: −0.117, SNDREAMS Diseased retinal images: −0.103). Our study showed that HFD method was the best method to calculate the fractal dimensions of normal and diseased retinal images.Item Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters(Elsevier, 2022-01) Raman, SundaresanProblems and diseases with eye are common in diabetic patients. Early diagnosis and detection of various diseases like retinopathy, neuropathy and nephropathy is crucial in diabetic patients. Certain demographic and diagnostic parameters play a significant role in predicting diseases related to diabetes. Development of a novel diagnostic method which helps to predict the disease by establishing a significant correlation with the demographic and diagnostic parameters is of prime importance. This study proposes a new methodology in which retinal fractals are obtained for the images and the derived retinal fractals are analysed to aid in disease prediction. This study comprises of images from patients with retinopathy, non retinopathy, neuropathy, nephropathy and hypertension. The proposed research is carried out in two aspects: 1) to correlate the retinal fractals of retinopathy and non retinopathy images with certain demographic and diagnostic parameters and interpret its significance, and 2) to exhibit a relationship between the retinal fractals and various diseases/addictive habit to facilitate the prediction of the disease/addictive habit. Hausdorff fractal dimension (HFD) was applied and higher fractal dimension was obtained for healthy cases. Then using Statistical Package for the Social Sciences (SPSS) various statistical parameters and significance were calculated to analyse the relationship. Analysis results showed that fractal value helped in distinguishing between the retinopathy and non retinopathy conditions. It also helped in diagnosing the presence and absence of hypertension. Correlation analysis between certain demographic parameters and fractal value showed a positive correlation whereas few exhibited negative correlation.