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Dinesh, P. M.
- Perceptually Weighted Color-to-Grayscale Conversion For Images With Non-Uniform Chromatic Distribution Using Multiple Regression
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1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
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ICTACT Journal on Image and Video Processing, Vol 11, No 2 (2020), Pagination: 2325-2330Abstract
Color-to-Gray scale conversion methods try to identify weights for various color channels to obtain a gray-scale image. These weights can be fixed either globally or computed on a localized basis. This paper presents an approach for computing the global weights using localized regions perpetually selected based on human perception. The approach aims to bring forth a color invariant gray scale conversion, such that it tries to maximize the required foreground information. The proposed method was tested on DIBCO-2013 dataset and qualitatively evaluated by looking at the structural similarity with the foreground using SSIM. The experimental results of ours and other color-to-gray scale methods have been tabulated and discussed.Keywords
Color-to-Grayscale Conversion, Multiple Regression, Least-Square Approach, Color Image, Gray Scale Image.
- Fundus Image Classification using Hybridized GLCM Features and Wavelet Features
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 3 (2021), Pagination: 2372-2375Abstract
We find the usefulness of computers in every field including medical field. Scanning the affected part has become a standard study. Diagnosing a disease at the right time, i.e. early detection, from the study of images enables the physician to take right decision and provide proper treatment to the patient. With the alarming growth of population, it is difficult for every individual patient to get a second opinion from medical expert. In these situations, computer-aided automatic diagnosis system will be much helpful. Diabetic retinopathy is a disorder that arises from increase in blood glucose level. Based on the severity, it has been distinguished into four stages. Diagnosing diabetic retinopathy at an early stage from retinal images and providing proper treatment will save the patient from severe vision loss. The proposed method adopts hybridized GLCM features and wavelet features to classify the fundus images according to the severity of the disease. The method is tested with fundus images collected from Indian Diabetic Retinopathy Dataset.Keywords
Fundus Image, GLCM, WDM Features, Diabetic Retinopathy, Classification.References
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