Informatics Publishing Limited

Asha Gowda Karegowda ¹, Sudeshna Bhattacharyya ¹, M. A. Jayaram ¹, A. S. Manjunath ²

Affiliations
1 Dept. of MCA, Siddaganga Institute of Technology, Tumkur, Karnataka, IN
2 Dept. of Computer Science, Siddaganga Institute of Technology, Tumkur, Karnataka, IN

Abstract

Exudates are one of the primary signs of diabetic retinopathy, which is the main cause of blindness and can be prevented with an early screening process. In this paper, KNNFP and WKNNFP classifiers have been used for automatic exudates detection. The publicly available diabetic retinopathy dataset DIARETDB1 has been used in the evaluation process. The RGB image is converted to HIS color space. The median filter is applied to intensity image of HIS for removal of noise followed by Contrast-Limited Adaptive Histogram Equalization to achieve uniform illumination. Further the optic disk is eliminated since optic disk has properties similar to exudates,which may cause the hindrance with exudates detection. Five pixel level features were selected as input for classfication of exudates and non-exudates pixels: hue from hue image and intensity, mean intensity, standard deviation of intensity and distance between mean of optic disk pixels and pixels of exudates and non-exudates extracted from the preprocessed Intensity image. KNNFP and WKNNFP classifiers have been experimented using two distance measures namely Euclidean distance and Manhattan distance. Investigation reveals that the performance of KNNFP using Euclidean distance is superior when compared to KNNFP using Manhattan distance. WKKNFP has been experimented using three attribute weight assignment methods:Relief, information gain and Gain ratio.Compared to KNNFP, there is substantial improvement of WKKFP performance by assigning the feature weight Gain ratio and Relief method. The classfication accuracy of WKNNFP is found to be 97.50% compared to classfication accuracy of 96.67% with KNNFP classifier.

Keywords

Diabetic Retinopathy, KNNFP and WKNNFP, Image Preprocessing, Exudates.

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M. A. Jayaram ¹, H. S. Vijaya Kumar ¹, D. P. Chaithanya ¹

Affiliations
1 Department of Master of Computer Applications, Siddaganga Institute of Technology, Tumkuru- 572103, Karnataka, IN

Abstract

Objectives: The objective of the work presented in this paper are two folded: (i) to extract novel features from retinal images, and (ii) to assess the degree of damage owing to diabetic maculopathy Methods: To achieve objectives set forth, hundred images inflicted with diabetic maculopathy were considered. The exudates in the macular region were identified in terms of numbers and the extent of their spread. In addition to this, the percolation of the lipid matters is also determined at the spots of exudates as a function of degree of yellowness. Findings: The significant finding which is first of its kind is estimation of approximate blockage area under direct vision. Applications: This research work has culminated in the development of a damage detection system. In the futuristic prospective the damage assessment system can be extended to be applicable for various kinds of diabetic related retinal damages.

Keywords

Central Vision, Damage Assessment System, Exudates, Fovea, Retinal Images, Macula.

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M. A. Jayaram ¹

Affiliations
1 Senior Professor, Department of Civil Engineering Siddaganga Institute of Technology, Tumakuru, Karnataka, IN

Abstract

This paper elaborates on optimized design of steel structures directed towards the sustainability of materials. The case in point is steel trusses that are extensively used structural components. Though copious research is available on use of conventional optimization methods, nature-inspired optimization algorithms have received scarce attention particularly in optimal design of planar trusses. In this paper, the development of Ant Lion algorithm for the optimal design models for steel trusses is elaborated. A comprehensive comparison with the optimized sectional weights obtained by other nature inspired optimization algorithms implemented in earlier research by the author. They include elitism based genetic algorithm (EBGA), ant colony optimization (ACO), artificial honeybee optimization (AHBO), and Particle swarm optimization (PSO) algorithm. Four steel trusses with different articulations have been considered for this purpose. It is found that the optimal weights obtained by Ant Lion algorithm are almost on par with those obtained by PSO. The other three algorithms vary marginally. However, the convergence to overall weight of trusses is different for different algorithms. ALO took 100-200 iterations for the convergence. In fact, the convergence to optimized weights are faster in case of ALO and PSO in relation to other algorithms.

Keywords

None

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