Department of Computer Science and Information Systems

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Author: search.filters.author.Raman, SundaresanHas files: No

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    Relationship of fractal analysis in retinal microvascularity with demographic and diagnostic parameters
    (Elsevier, 2022-01) Raman, Sundaresan
    Problems 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.
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    Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach
    (Wolters Kluwer, 2022-09) Raman, Sundaresan
    Infectious keratitis, especially viral keratitis (VK), in resource-limited settings, can be a challenge to diagnose and carries a high risk of misdiagnosis contributing to significant ocular morbidity. We aimed to employ and study the application of artificial intelligence-based deep learning (DL) algorithms to diagnose VK.
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    The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review
    (MDPI, 2022-10) Raman, Sundaresan
    In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.
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    An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization
    (PLOS One, 2023-04) Raman, Sundaresan
    Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.
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    Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India
    (MDPI, 2023-06) Raman, Sundaresan
    This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier’s importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.
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    Use of artificial intelligence algorithms to predict systemic diseases from retinal images
    (Wiley, 2023-05) Raman, Sundaresan
    The rise of non-invasive, rapid, and widely accessible quantitative high-resolution imaging methods, such as modern retinal photography and optical coherence tomography (OCT), has significantly impacted ophthalmology. These techniques offer remarkable accuracy and resolution in assessing ocular diseases and are increasingly recognized for their potential in identifying ocular biomarkers of systemic diseases. The application of artificial intelligence (AI) has been demonstrated to have promising results in identifying age, gender, systolic blood pressure, smoking status, and assessing cardiovascular disorders from the fundus and OCT images. Although our understanding of eye–body relationships has advanced from decades of conventional statistical modeling in large population-based studies incorporating ophthalmic assessments, the application of AI to this field is still in its early stages. In this review article, we concentrate on the areas where AI-based investigations could expand on existing conventional analyses to produce fresh findings using retinal biomarkers of systemic diseases. Five databases—Medline, Scopus, PubMed, Google Scholar, and Web of Science were searched using terms related to ocular imaging, systemic diseases, and artificial intelligence characteristics. Our review found that AI has been employed in a wide range of clinical tests and research applications, primarily for disease prediction, finding biomarkers and risk factor identification. We envisage artificial intelligence-based models to have significant clinical and research impacts in the future through screening for high-risk individuals, particularly in less developed areas, and identifying new retinal biomarkers, even though technical and socioeconomic challenges remain. Further research is needed to validate these models in real-world setting.
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    Quality Isosurface Mesh Generation Using an Extended Marching Cubes Lookup Table
    (Wiley, 2008-09) Raman, Sundaresan
    The Marching Cubes Algorithm may return degenerate, zero area isosurface triangles, and often returns isosurface triangles with small areas, edges or angles. We show how to avoid both problems using an extended Marching Cubes lookup table. As opposed to the conventional Marching Cubes lookup table, the extended lookup table differentiates scalar values equal to the isovalue from scalar values greater than the isovalue. The lookup table has 38= 6561 entries, based on three possible labels, ‘−’ or ‘=’ or ‘+’, of each cube vertex. We present an algorithm based on this lookup table which returns an isosurface close to the Marching Cubes isosurface, but without any degenerate triangles or any small areas, edges or angles.
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    Layers for effective volume rendering
    (ACM Digital Library, 2008) Raman, Sundaresan
    A multi-layer volume rendering framework is presented. The final image is obtained by compositing a number of renderings, each being represented as a separate layer. This layer-centric framework provides a rich set of 2D operators and interactions, allowing both greater freedom and a more intuitive 2D-based user interaction. We extend the concept of compositing which is traditionally thought of as pertaining to the Porter and Duff compositing operators to a more general and flexible set of functions. In addition to developing new functional compositing operators, the user can control each individual layer's attributes, such as the opacity. They can also easily add or remove a layer from the composition set, change their order in the composition, and export and import the layers in a format readily utilized in a 2D paint package. This broad space of composition functions allows for a wide variety of effects and we present several in the context of volume rendering, including two-level volume rendering, masking, and magnification. We also discuss the integration of a 3D volume rendering engine with our 2.5D layer compositing engine.
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    Analysis of spatial variation of nuclear morphology in tissue microenvironments
    (IEEE, 2010) Raman, Sundaresan
    We present a study of the spatial variation of nuclear morphology of stromal and cancer-associated fibroblasts in the mouse mammary gland. The work is part of a framework being developed for the analysis of the tumor microenvironment in breast cancer. Recent research has uncovered the role of stromal cells in promoting tumor growth and progression. In specific, studies have indicated that stromal fibroblasts - formerly considered to be passive entities in the extra-cellular matrix - play an active role in the progression of tumor in mammary tissue. We have focused on the analysis of the nuclear morphology of fibroblasts, which several studies have shown to be a critical phenotype in cancer. An essential component of our approach is that the nuclear morphology is studied within the 3D spatial context of the tissue, thus enabling us to pose questions about how the locus of a cell relates to its morphology, and possibly to its function. In order to make quantitative comparisons between nuclear populations, we build statistical shape models of cell populations and infer differences between the populations through these models. We present our observation on both normal and tumor tissues from the mouse mammary gland.
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    Analyzing Surface Defects in Apples Using Gabor Features
    (IEEE, 2016) Raman, Sundaresan
    This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector, combinations of Gabor Wavelets with different feature descriptors were explored. Comparative study has been carried out between Haralick features, Local Binary Patterns, and kernel PCA, to observe their performance over Gabor features. Classification is achieved via Support Vector Machines and K-Nearest Neighbors. For the task of disease detection, accuracy recorded was greater than 96.9% for Gabor+LBP approach and in range of 89.8% to 96.25% for Gabor+Haralick approach. Gabor+kernel PCA recorded lowest accuracy of 90%. For disease identification, combination of Gabor+LBP outperformed other combinations, recording highest accuracy ranging from 85.93% to 95.31%.