Robust multi-scale superpixel classification for optic cup localization

Tan, Ngan Meng; Xu, Yanwu; Goh, Wooi Boon; Liu, Jiang

doi:10.1016/j.compmedimag.2014.10.002

Cited by 45 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thirdly, they introduced an affiliation refinement plot that uses both auxiliary (refinement) priors and neighbourhood (local) setting. Tried on a clinical dataset of ORIGA light involving 650 images, the proposed strategy accomplishes 0.081 distinct error CDR, a 26.7% non‐overlap ratio (proportion) and a simple however widely used investigative measure (Tan, Xu, Goh, & Liu, ). In computer vision applications, super‐pixels (SP)—due to the upgraded introduction above pixel‐based techniques—are ending up plainly progressively acknowledged.…”

Section: Pattern Classification and Machine Learning Methods For Glaumentioning

confidence: 99%

Fundus image classification methods for the detection of glaucoma: A review

Saba

Bokhari

Sharif

et al. 2018

Microscopy Res & Technique

View full text Add to dashboard Cite

Glaucoma is a neurodegenerative illness and is considered as a standout amongst the most widely recognized reasons for visual impairment. Nerve's degeneration is an irretrievable procedure, so the diagnosis of the illness at an early stage is an absolute requirement to stay away from lasting loss of vision. Glaucoma effected mainly because of increased intraocular pressure, if it is not distinguished and looked early, it can result in visual impairment. There are not generally evident side effects of glaucoma; thus, patients attempt to get treatment just when the seriousness of malady is advanced altogether. Determination of glaucoma often comprises of review of the basic crumbling of the nerve in conjunction with the examination of visual function capacity. This article shows the persistent illustration of glaucoma, its side effects, and the potential people inclined to this malady. The essence of this article is on different classification methods being utilized and proposed by various scientists for the identification of glaucoma. This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma. The research related to the findings and the treatment is likewise evaluated in this article.

show abstract

Section: Pattern Classification and Machine Learning Methods For Glaumentioning

confidence: 99%

Fundus image classification methods for the detection of glaucoma: A review

Saba

Bokhari

Sharif

et al. 2018

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…Ela particiona a imagem em vários agrupamentos de pixels, fazendo com que as imagens sejam analisadas por regiões. Cheng et al (2013) Tan et al (2015) utilizaram um pré-processamento nas imagens com o objetivo de extrair os vasos sanguíneos para que em seguida seja aplicado uma normalização de contraste nas imagens. Após essa etapa, é realizado uma segmentação com o uso do superpixel em multi-escala, a qual é realizada pelo algoritmo SLIC.…”

Section: Superpixelunclassified

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

Claro

Veras

Santos

et al. 2018

RBCA

View full text Add to dashboard Cite

A utilização de técnicas de processamento digital de imagens (PDI) é destaque no cenário médico para o diagnóstico automático de patologias. Na área oftalmológica o glaucoma é a segunda principal causa da perda de visão no mundo e não possui cura. Atualmente, existem tratamentos para prevenir a perda da visão, contudo a doença deve ser descoberta nos estágios iniciais. O objetivo principal deste artigo é revisar as metodologias e técnicas de segmentação dos limites do disco óptico e escavação. Essas regiões são utilizadas para o cálculo de métricas para classi cação do glaucoma e auxílio aos pro ssionais da área. Os trabalhos mais recentes publicados na área foram classi cados em cinco grupos de acordo com a principal técnica de PDI aplicada: agrupamento, superpixel, contorno ativo, morfologia matemática e redes neurais convolucionais. Além disso, foi realizado um levantamento das principais bases de imagens e métricas de avaliação utilizadas.Palavras-Chave: Algoritmos de Agrupamento; Contorno Ativo; Glaucoma; Morfologia matemática; Redes neurais convolucionais; Superpixel. Abstract The use of digital image processing techniques (DIP) is highlighted in the medical scenario for automatic diagnosis of pathologies. In the ophthalmologic area, glaucoma is the second leading cause of vision loss in the world

show abstract

“…The initial input of each phase is the ONH image. It similar to the setup of the work in [22][23]. In the learning phase, data class (normal/glaucoma) is required as the input to give a label against the ONH image to determine whether the image is normal or glaucoma.…”

Section: Methodsmentioning

confidence: 99%

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Septiarini¹,

Hamdani²,

Khairina³

2016

IJECE

View full text Add to dashboard Cite

Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%. Keyword: Contour features Cup Fundus image Glaucoma Morphology Copyright © 2016 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Anindita Septiarini, Faculty of Computer Science and Information Technology, Universitas Mulawarman, Samarinda-75123, Indonesia. Email: anindita@unmul.ac.id INTRODUCTIONGlaucoma is an eye disease that can cause vision problems. This disease is the second cause of blindness in the world [1], therefore the detection of glaucoma is required. The detection of glaucoma by an expert carried out by observing the structure of the retina using a fundus image, to determine the eyes of the patient are normal and glaucoma. Currently, research on automatic glaucoma detection techniques have been developed. Most of the research using fundus images as input data and several feature extraction methods have been applied.Several previous researches related to automatic glaucoma detection generated by applying nonmorphological feature extraction method (image-based features). The feature referred to color, shape or texture on the optic nerve head (ONH), retinal nerve fiber layer (RNFL) or blood vessel as part of the structure of the retina [2]. These parts undergo changes in patients with glaucoma [3]. ONH (also called disc) is the part area with round shape. Inside of the disc, there is a smaller area with round shape called a cup and a neuroretinal rim which is an area that lies between the disc and the cup. RNFL is the area outside of the disk where the structure of thickness of the RNFL is the one which distinguishes whether a patient's eye is normal or glaucoma. The illustration of ONH and RNFL structure are shown in Figure 1.In the features extraction of the ONH applied the process of blood vessel inpainting as the previous phase in order to remove or blurring the blood vessel that presences as noise. This process is required the detection to determine the presence of the blood vessel. Several methods have been proposed for the detection of the blood vessel. Thresholding with the low pixel values in the image followed by median filtering of the size 41x41 [4], using threshold probing [5] and normalized mathematical morphology (used to enhance the vessel...

show abstract

Robust multi-scale superpixel classification for optic cup localization

Cited by 45 publications

References 19 publications

Fundus image classification methods for the detection of glaucoma: A review

Fundus image classification methods for the detection of glaucoma: A review

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Contact Info

Product

Resources

About