U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity

Ramachandran, Sivakumar; Strisciuglio, Nicola; Vinekar, Anand; John, Richard R.; Azzopardi, George

doi:10.1007/s00521-019-04697-6

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…Literature features a large volume of research works on eye imaging. When compared to retinabased blood vessel segmentation studies [6][7][8][9], the amount of research publications on tear film imaging is very less. The research contributions [5,10,11] published by VARPA group are the only reference available for this study.…”

Section: Related Workmentioning

confidence: 99%

A Novel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

Abraham¹,

Mohan²,

Shine³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Tear film, the outermost layer of the eye, is a complex and dynamic structure responsible for tear production. The tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular surface. Dry eye syndrome (DES) is a symptomatic disease caused by reduced tear production, poor tear quality, or excessive evaporation. Its diagnosis is a difficult task due to its multifactorial etiology. Out of several clinical tests available, the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES diagnosis. An instrument known as Tearscope Plus allows the rapid assessment of the lipid layer. A grading scale composed of five categories is used to classify lipid layer patterns. The reported work proposes the design of an automatic system employing light weight convolutional neural networks (CNN) and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope Plus. The designed framework achieves promising results compared with the existing state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

A Novel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

Abraham¹,

Mohan²,

Shine³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…1c, respectively. A curved receptive field was configured in [35], to detect high curvature points of the retinal vessel tree. In [40,42], the authors demonstrated that a bank of B-COSFIRE filters, configured to delineate lines of different thickness, can be used as feature extractors and combined with a classifier to perform complex decisions.…”

Section: Edge and Line Detectionmentioning

confidence: 99%

Brain-Inspired Algorithms for Processing of Visual Data

Strisciuglio

Petkov

2021

Lecture Notes in Computer Science

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The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

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“…1c, respectively. A curved receptive field was configured in [35], to detect high curvature points of the retinal vessel tree. In [42,40], the authors demonstrated that a bank of B-COSFIRE filters, configured to delineate lines of different thickness, can be used as feature extractors and combined with a classifier to perform complex decisions.…”

Section: Edge and Line Detectionmentioning

confidence: 99%

Brain-inspired algorithms for processing of visual data

Strisciuglio¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing. In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

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U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity

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Cited by 12 publications

References 45 publications

A Novel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

A Novel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

Brain-Inspired Algorithms for Processing of Visual Data

Brain-inspired algorithms for processing of visual data

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