An adaptive threshold based image processing technique for improved glaucoma detection and classification

Issac, Ashish; Mangipudi, Parthasarathi; Dutta, Malay Kishore

doi:10.1016/j.cmpb.2015.08.002

Cited by 174 publications

(74 citation statements)

References 16 publications

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“…The segmentation step emerges as optional. Yet the preprocessing is performed in different approaches: image size reduction [38][39][40], image channels manipulation [30,45,50], histogram equalization and noise filtering [48], histogram of visual words [49] or bilateral filtering [51]. All the techniques were applied in order to highlight the OD and the OD-cup region.…”

Section: Discussionmentioning

confidence: 99%

“…In the segmentation process, Issac et al [40] segmented the OD and OD-cup using the red channel. In this manner, the morphological dilatation was the differential of this process.…”

Section: Discussionmentioning

confidence: 99%

“…Still, in 2015, Issac et al [40] developed an adaptive threshold-based method to segment the OD and OD-cup for glaucoma diagnosis. First, the ONH region was analyzed.…”

Section: Methods Using Features Extraction For Glaucoma Diagnosismentioning

confidence: 99%

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Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods:The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized.Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

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Section: Discussionmentioning

confidence: 99%

“…In the segmentation process, Issac et al [40] segmented the OD and OD-cup using the red channel. In this manner, the morphological dilatation was the differential of this process.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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“…A segmentation based on vessel kinks was developed by Damon et al3 to detect the cup using the patches within the ONH. Issac et al4 proposed an automatic threshold segmentation-based method for detecting OC using local features of the fundus image. Liu et al5 presented a cup segmentation technique where the threshold-initialization–based level set was processed.…”

Section: Introductionmentioning

confidence: 99%

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

Almazroa

Alodhayb²,

Raahemifar

et al. 2017

OPTH

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We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu’s segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

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“…The main advantage of this method was, it selected the best image for ACG diagnosis with high accuracy. Issac et al 31 developed an adaptive threshold-based segmentation technique to improve the detection and classi¯cation of Glaucoma. This work comprises the following stages:…”

Section: Classi¯cationmentioning

confidence: 99%

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Balakrishnan

2017

J. Innov. Opt. Health Sci.

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Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include ine±cient, inaccurate and estimates only the a®ected area. In order to solve these issues, a Neighboring Di®erential Clustering (NDC) -Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At¯rst, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identi¯cation. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, speci¯city, accuracy, Hausdor® distance, Jaccard and dice metrics.

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An adaptive threshold based image processing technique for improved glaucoma detection and classification

Cited by 174 publications

References 16 publications

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

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