A novel color space of fundus images for automatic exudates detection

Khojasteh, Parham; Aliahmad, Behzad; Kumar, Deepak

doi:10.1016/j.bspc.2018.12.004

Cited by 22 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khojasteh, et al, 2019, authors have developed a new color space for automatic exudate detection. Based on the research findings [ 94 ], this study presented a new color space for fundus images with three channels. Thus, this article shows that the selection of color space is a critical consideration when doing fundus image analysis.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

show abstract

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Other methods have been suggested for EXs segmentation using CNN method. Some authors, such as P. Khojasteh et al [17], who developed fine segmenting and pre-trained of CNN methods for EXs related features segmentation. The extensive study for detecting EXs various pathological sign in EXs has been proposed.…”

Section: Previous Related Workmentioning

confidence: 99%

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Wisaeng

2023

Preprint

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is one of the earliest signs of a complication of diabetes mellitus and is also a leading cause of visual blindness globally. Recent studies have proposed that exudates (EXs) are the hallmark of DR severity. The present study aims to accurately and automatically detect EXs related features that are difficult to detect in fundus photography in early stages. The proposed method utilizes a Fusion of Histogram-Based Fuzzy C-Means clustering (FHBFCM) based on a New Weight Assignment Scheme (NWAS), and a set of four selected features from stages of pre-processing in order to evolve the detection method. Through the coarse segmentation stage, the optimal parameter of FHBFCM is trained by the features of DR for detection of EXs diseases through a stepwise enhancement process. The histogram-based is used to find the color intensity in each of the pixels and performed to accomplish RGB color information. This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by FCM method. Afterwards, the best expression of NWAS is then used for fine segmentation stage. The experimental results show that the proposed method achieves accuracy values of 96.12%, 97.20%, 93.22% for EXs segmentation on DiaretDB0, DiaretDB1, and STARE fundus image datasets respectively. In conclusion, this study provides a new method for early detection of EXs related features with a competitive accuracy and its ability to outperforms the other stateof-the-art by optimizing the detection quality, and possibly reducing the false positive segmentation in a significant way.

show abstract

“…A new technique for exudates detection was implemented based on color space representation and identification in color retinal fundus images. First, Khojasteh et al 61 designed and developed the color using RGB color space orthogonal transformation. Then, various RGB color space combinations were compared, and 20‐fold cross‐validation was used for better performance.…”

Section: Image Processing Techniques Applied In Selected Research Art...mentioning

confidence: 99%

“…First, Khojasteh et al 61 designed and developed the color using RGB color space orthogonal transformation. Then, various RGB color space combinations were compared, and 20-fold cross-validation was used for better performance.…”

Section: Hard and Soft Exudates Detection Techniquementioning

confidence: 99%

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

View full text Add to dashboard Cite

Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

show abstract

A novel color space of fundus images for automatic exudates detection

Cited by 22 publications

References 43 publications

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Exudates Detection Using Fusion of Histogram-Based Fuzzy C-Means Clustering Based on a New Weight Assignment Scheme

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Contact Info

Product

Resources

About