Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Haleem, Muhammad Salman; Han, Liangxiu; Hemert, Jano van; Li, Baihua; Fleming, Alan

doi:10.1109/jbhi.2014.2352271

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…The dataset is collected by diseased and healthy retinal images. Many diseased retinal images are collected from Diabetic Retinopathy patients [3]. The results and the accuracies of RBFN by dice-coefficient like evaluation metric.…”

Section: Resultsmentioning

confidence: 99%

“…Superpixel are used to capture image redundancy and support the convenient primitive image pattern. [3] In this paper, Superpixel is generated by the adaptive SLIC (SLICO) which is similar to the SLIC. SLIC uses some compactness parameter which is chosen by user for every superpixel in the image.…”

Section: Superpixel Generationmentioning

confidence: 99%

“…Automated analysis of retinal images have the ability to reduce the time and also it must be detected the problem of the retinal part very easily. Retinal area obtained from the imaging instruments such as fundus camera and scanning laser opthalmoscope (SLO) which contain structure of retinal area with artefacts (eyelashes and eyelids) [3].…”

Section: Introductionmentioning

confidence: 99%

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Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

Singh¹,

Gill²

2016

IJECE

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<p class="Abstract">Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to detect the true retinal area. Artefacts like eyelids and eyelashes are come along with retinal part so removal of artefacts is the big task for better diagnosis of disease into the retinal part. In this paper, we have proposed the segmentation and use machine learning approaches to detect the true retinal part. Preprocessing is done on the original image using Gamma Normalization which helps to enhance the image that can gives detail information about the image. Then the segmentation is performed on the Gamma Normalized image by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Superpixel is used to reduce the complexity of image processing task and provide suitable primitive image pattern. Then feature generation must be done and machine learning approach helps to extract true retinal area. The experimental evaluation gives the better result with accuracy of 96%.</p>

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Section: Resultsmentioning

confidence: 99%

Section: Superpixel Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

Singh¹,

Gill²

2016

IJECE

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“…The presence of noise in retinal images can render them inappropriate for identification phase [28], [29]. Retinal images contain non-uniform illuminations, blurry areas, and noisy background.…”

Section: A Pre-processingmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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“…On the off chance that retinopathy is identified sufficiently early, treatment can stop it deteriorating. Something else, indications become perceptible with time, moreover, it is more difficult to treat [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Eye Screening Method Based on a Multi-Anatomical Retinal System Segmentation with the Help of Morphology & Fuzzy Logic

Vitta¹,

Basha²

2019

IJITEE

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Eye examination is used to determine Retinal problems at an early stage. Retina screening can be used to detect various hidden retinal problems including pre-diabetes and diabetes. The doctors will depend on segmentation results of retinal structures to determine the abnormalities in the eye. A novel algorithm for instant identification and segmentation of the optic disk (OD) and detection of exudates in human retinal images is described in this document. The proposed technique uses segmentation methodology for automatically detecting of these structures. This methodology uses morphology theories and fuzzy techniques to detect the structures even in pathological images. The performance is evaluated using two datasets that is DRISHI-GS (optic disc) and DIARETD1 (exudates). Present methods have accuracy and efficiency is less, but in the proposed new novel method i.e morphology based fuzzy logic, we achieved an accuracy 92.91% and PPV (positive predictive value) is 89.54% in optic disc. specificity and F-score also trained with help of subjected method 99% and 93.17% in exudate .

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Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Cited by 19 publications

References 22 publications

Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Automatic Eye Screening Method Based on a Multi-Anatomical Retinal System Segmentation with the Help of Morphology & Fuzzy Logic

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