An image based approach to detect Retinopathy of prematurity

Prabakar, S.; Porkumaran, K.; Guna, Sundari J

doi:10.1109/sibircon.2010.5555322

Cited by 3 publications

(4 citation statements)

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“…28 It starts by manually selecting the vessel to be analyzed and performs a quantitative assessment of the width of the vessels and their tortuosity, based on the number of pixels with similar intensity. With regard to automatic proposals, the work by Prabakar et al 29 proposes a computerized method to help diagnose ROP based on digital fundus image. A matched filter, as well as smoothing by median filter, erosion, and dilation, is used to characterize and segment the retinal vasculature.…”

Section: Related Workmentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

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Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.

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Section: Related Workmentioning

confidence: 99%

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Intriago-Pazmiño

Ibarra-Fiallo

Crespo³

et al. 2020

Health Informatics J

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“…As per our knowledge, work done by the researchers is focused on blood vessel detection and tortuosity and limited work has been reported on the classification of stages based on the severity of the disease. Prabakar et al [11] used the histogram approach using limited dataset for classification, and no performance metrics were discussed in the results. Rebecca Rollins et al [56] classified the images into three categories No ROP (NR), ROP not requiring treatment (RNT), and ROP which required treatment (RT).…”

Section: Discussionmentioning

confidence: 99%

“…Another set of studies discuss image localization, feature extraction, and classification using neural network and could classify only three stages: stage 1, stage 2, and stage 3 [11]. They could establish use of segmentation of vessels to develop a supervised classification for ROP.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the research work is carried on retinal grading of blood vessels, tracing of vessels [4] diameter, curvature [5], length to chord [6], angle-based measures [7][8][9], Principal Component Analysis (PCA) [10], tortuosity measurement for the detection of pre-plus, plus disease, and normal and limited work on the automation of classification of stages based on severity [11,12]. The challenges faced for the development of automation of classification of stages are ROP dataset that is limited and extremely imbalanced dataset due to lack of awareness and expertise.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Staging and Grading for Retinopathy of Prematurity on Indian Database

KADGE,

Nalbalwar,

Nandgaokar

et al. 2023

JAIT

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Retinopathy of prematurity (ROP) is a disorder of the retina in neonates. If ROP is not treated at early stage, neonates’ vision is affected, leading to blindness. It is necessary to diagnose and treat ROP at earliest. Several ROP assessment techniques based on Image analysis have been introduced in recent years. These studies identify only normal, abnormal and plus disease. This research article explores the identification of distinct ROP stages along with normal and abnormal detection. Detecting the stages will help to expedite the treatment and prevent vision loss. The proposed framework consists of feature extraction using Scale Invariant Feature Transform (SIFT) and Pyramid Histogram of Words (PHOW) techniques. Three efficient supervised machine learning algorithms, namely random forest (RF), support vector machine (SVM) and extreme boosting gradient (XGBoost), are used to classify different stages of ROP. A data set captured by RetCam 3 is used to evaluate the model. Based on rigorous evaluation, the accuracy of different ROP stages is 93.68%, 83.33%, 85.71%, 55.55% and 100% for normal, stage 1, 2, 3 and 4, respectively.

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