Retinal Image Enhancement in Multi-mode Histogram

Intajag, S.; Tipsuwanporn, V.; Chatthai, R.

doi:10.1109/csie.2009.610

Cited by 18 publications

(10 citation statements)

References 6 publications

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“…The ultimate aim in a large number of image processing applications is to extract important features from the image data, from which a description, interpretation, and/or understanding of the scene can be obtained for human viewers, or to provide 'better' input for other automated image processing techniques [8]. Enhancement is a fundamental task in digital image processing and analysis, aiming to improve the appearance of image in terms of human brightness perception [9]. Image enhancement techniques are designed to improve the quality of an image as perceived by a human being.…”

Section: Overview Of Image Processing Methodsmentioning

confidence: 99%

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

Kumar

Shaik²

2015

Image Processing in Diabetic Related Causes

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Diabetes mellitus (DM) often simply known as diabetes-is a group of metabolic diseases. The most burning problem in India is 'Metabolic Syndrome'. One of these is the DM, calling India the diabetes Capital of the world as there is an alarming rise in prevalence of diabetes, which has gone beyond epidemic to the pandemic one. For every 10 s a person dies from diabetic related causes in the world. DM is a metabolic disorder which characterizes the inability of the pancreas to control blood glucose concentration. This problem results in blood glucose levels moving out of normal range [1].The classification of diabetes falls under 3 categories:

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Section: Overview Of Image Processing Methodsmentioning

confidence: 99%

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

Kumar

Shaik²

2015

Image Processing in Diabetic Related Causes

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“…Any single technique cannot be considered superior in the applications of image processing, but the test and error method can be used as a practical process to attain the perfect results. Image enhancement is the most basic requirement in the processing and analysis of digital images to improve the appearance of the image in terms of perception for human cognition [8]. Image enhancement is used as the preprocessing procedure and segmentation is primarily valuable in the categorization of objects and labeling of image extracted features for easy post-processing investigation.…”

Section: Overview Of Image Processingmentioning

confidence: 99%

Histogram Equalized Thresholding method for analysis of Diabetic Myonecrosis related images

Shaik¹,

Bommagani²

2021

helix

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Diabetes Mellitus has become a common disorder in all groups of people throughout the world in the current days, mainly because of sedentary lifestyles and abnormal eating habits. Moreover, it has become a threat as well as a challenge to the researchers in the scientific society to find a solution to control this problem. Even though there is no proven cure for this disorder, one can lead a normal life with knowledge and awareness of issues related to diabetes. The main issue of concern for people with diabetes is its related complications, especially Myonecrosis. Diabetic myonecrosis is a rare complication of diabetes mellitus that has been poorly addressed. The disease presents itself in a human as lower extremity acute non-traumatic swelling and pain that can imitate deep vein thrombosis (DVT). Usually, the medical condition is self-limiting, and patients react well to therapeutic treatment that supports them. Together with other microvascular complications such as retinopathy, nephropathy, or neuropathy, it often develops into further complications. These complications are more common among Type I diabetics but can also arise in patients with Type II diabetes. Current image processing methodologies hold a significant position in removing specific challenges related to medical imaging technologies. The research work dealt with in this paper is about the processing of images related to Diabetic Myonecrosis, and the outcome results are used for analysis of the problem in various conditions of DM patient with Myonecrosis. Threshold segmentation methods are used to extract the features, and several attributes have been derived for statistical comparison in this work. The tools used for investigation in this work are MATLAB Technical computing language for simulation results and MIPAV for deriving statistical parameters.

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“…However, these mechanisms have produced spot noise and increased the luminance of the brighter features, i.e., the optic disc and cup. Intajag et al [21] considered a fuzzy set with a derivation equation to partition the histogram into multimodes for image enhancement. They mainly applied a fuzzy set on each mode of histogram to get an optimal crossover point of the S-function with the highest probability of the fuzzy index, which was then utilized for enhancement.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Qureshi

Shaheed

2019

Algorithms

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Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.

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Retinal Image Enhancement in Multi-mode Histogram

Cited by 18 publications

References 6 publications

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

Histogram Equalized Thresholding method for analysis of Diabetic Myonecrosis related images

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

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