Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

Bechar, Mohammed El Amine; Settouti, Nesma; Barra, Vincent; Chikh, Mohammed Amine

doi:10.1007/s11045-017-0483-y

Cited by 38 publications

(20 citation statements)

References 34 publications

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“…In order to evaluate whether our proposed method is feasible and comparable to the most recent published optic cup segmentation algorithms [35–37], also 39 glaucoma images from the public database RIM-ONE were experimented. F-score and CDR acquired by our proposed method and these referenced algorithms were listed in Table 6.…”

Section: Resultsmentioning

confidence: 99%

An efficient optic cup segmentation method decreasing the influences of blood vessels

et al. 2018

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BackgroundOptic cup is an important structure in ophthalmologic diagnosis such as glaucoma. Automatic optic cup segmentation is also a key issue in computer aided diagnosis based on digital fundus image. However, current methods didn’t effectively solve the problem of edge blurring caused by blood vessels around the optic cup.MethodsIn this study, an improved Bertalmio–Sapiro–Caselles–Ballester (BSCB) model was proposed to eliminate the noising induced by blood vessel. First, morphological operations were performed to get the enhanced green channel image. Then blood vessels were extracted and filled by improved BSCB model. Finally, Local Chart-Vest model was used to segment the optic cup. A total of 94 samples which included 32 glaucoma fundus images and 62 normal fundus images were experimented.ResultsThe evaluation parameters of F-score and the boundary distance achieved by the proposed method against the results from experts were 0.7955 ± 0.0724 and 11.42 ± 3.61, respectively. Average vertical optic cup-to-disc ratio values of the normal and glaucoma samples achieved by the proposed method were 0.4369 ± 0.1193 and 0.7156 ± 0.0698, which were also close to those by experts. In addition, 39 glaucoma images from the public dataset RIM-ONE were also used for methodology evaluation.ConclusionsThe results showed that our proposed method could overcome the influence of blood vessels in some degree and was competitive to other current optic cup segmentation algorithms. This novel methodology will be expected to use in clinic in the field of glaucoma early detection.

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Section: Resultsmentioning

confidence: 99%

An efficient optic cup segmentation method decreasing the influences of blood vessels

et al. 2018

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“…The input of classification network is the entire RNFL thickness vector calculated from pixel-wise segmentation results. Different from super pixel segmentation adopted by current methods of automatic glaucoma diagnosis [1,19], pixel to pixel labeling keeps complete and accurate information. Muhammad et al [38] used convolutional neural network to extract features from OCT images to diagnose glaucoma.…”

Section: Discussionmentioning

confidence: 99%

“…Glaucoma is the leading cause of irreversible blindness [1], and the early detection of glaucoma is of great significance to cure this disease. Elevated intraocular pressure (IOP), visual field (VF) defect and glaucomatous optic neuropathy (GON) yield three main clinical symptoms for glaucoma diagnosis [2].…”

Section: Introductionmentioning

confidence: 99%

“…Most of previous machine learning methods used in glaucoma diagnosis adopt either the vertical cup to disc ratio (CDR) or RNFL thickness as the feature [1,19] via super pixel segmentation, which is inevitably incomprehensive compared with pixel-wise segmentation. What is worse, current RNFL thickness measurements in clinical OCT reports are summarized in 4 quadrants, 8 quadrants or 12 clock hours [19,20], or in thickness maps [21].…”

Section: Introductionmentioning

confidence: 99%

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Joint retina segmentation and classification for early glaucoma diagnosis

Wang

et al. 2019

Biomed. Opt. Express

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We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.

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“…For instance, authors in [ 50 ] process only the grey level plane J. Methods illustrated in [ 34 ] and [ 48 ] would not work in images where colour differences between OD and background are not significant. The computation of colour derivatives, only in certain pixels located in a radius centred on the OD, was presented in [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic CDR Estimation for Early Glaucoma Diagnosis

Fernández-Granero

Sarmiento

Morillo

et al. 2017

Journal of Healthcare Engineering

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Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

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Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

Abstract: International audienc

Cited by 38 publications

References 34 publications

An efficient optic cup segmentation method decreasing the influences of blood vessels

An efficient optic cup segmentation method decreasing the influences of blood vessels

Joint retina segmentation and classification for early glaucoma diagnosis

Automatic CDR Estimation for Early Glaucoma Diagnosis

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