Diagnosis of plus diseases for the automated screening of retinopathy of prematurity in preterm infants

Sivakumar, R.; Eldho, Manu; Jiji, C. V.; Vinekar, Anand; John, Richard R.

doi:10.1109/iciinfs.2016.8262975

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…A novel approach was introduced by the author using a matched filter. The author presented a semiautomated CAD method for plus disease assessment [40]. Vessel segmentation which is one of the important features is extracted using convolutional neural networks [41].…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Automated Staging and Grading for Retinopathy of Prematurity on Indian Database

KADGE,

Nalbalwar,

Nandgaokar

et al. 2023

JAIT

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“…-------------------1 III. VESSEL WIDTH CALCULATIONS Binarized form of retinal image is taken for width computation then from the binarized skeleton largest circle fits inside white pixels region, the average of all that circles that is calculated as a width of that image, presented by Sivakumar et al, [8]. In this work employed two methods to measure the vessel width in pre-processed retinal image.…”

Section: Pre-processingmentioning

confidence: 99%

Retinal Image Vessel Width Analysis in Retinopathy of Prematurity

Manjunatha¹,

Sheshadri²

2019

AJCST

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This paper aims at development of Oscillating Pendulum Based Algorithm (OPBA) for retinal image width computation and analysis for the investigation of Retinopathy of prematurity (ROP). The algorithms have been explained in detail with theoretical investigations and simulations, images from local hospital data base were considered in the investigation. Further this paper also presents an overview of direct retinal vessel width computation method along with the comparative analysis. The results obtained are found to be encouraging for analysis of plus disease under ROP.

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