Texture Classification of Retinal Layers in Optical Coherence Tomography

Baroni, M.; Diciotti, Stefano; Evangelisti, A.; Fortunato, Pina; Torre, A. La

doi:10.1007/978-3-540-73044-6_220

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…IR consists of the retinal nerve fiber layer, the inner ganglion cell layer (GCL), the inner plexiform layer (IPL), and the inner nuclear layer (INL). OR encompasses the outer plexiform layer (OPL), the outer nuclear layer (ONL), and the inner (IS) and outer (OS) segment photoreceptor layers [3].…”

Section: Introductionmentioning

confidence: 99%

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Yazdanpanah¹,

Hamarneh

Smith

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis.

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Section: Introductionmentioning

confidence: 99%

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Yazdanpanah¹,

Hamarneh

Smith

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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“…Texture analyses have been used to segment inner and outer retinal layers by means of artificial neural networks. While it seems to work with abnormal images, it only detects two layers 12. A basic edge detection kernel based on the first derivative of Gaussian in the vertical direction, supported by non-maxima suppression and hysteresis thresholding to detect edge points has been used to label up to six retinal layers 13.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography

Serrano‐Aguilar

Abreu²,

Antón-Canalís

et al. 2011

Br J Ophthalmol

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“…Since, calculating the thickness of retina layers allows to determine the thickness of the whole retina that it will be very efficient for diagnosis of retina pathologies, in this work, two main retina layers (Fig2) are considered: the inner retina (IR), enclosing retinal nerve fiber layer, inner ganglion cells and inner plexiform layer, and the outer retina (OR), including outer plexiform and photoreceptor layers (thicker in fovea). In fact, they represent the most important features in clinical applications [1].…”

Section: Introductionmentioning

confidence: 99%

“…In a same work [1], characteristics of co-occurrence matrices of gray levels have been used for discriminating among main retinal layers that for inner retinal layers, accuracy was 79% precision, slightly lower values were obtained for outer retinal layers.…”

Section: Introductionmentioning

confidence: 99%

An image processing approach to automatic detection of retina layers using texture analysis

Naseri

Pouyan

Kavian³

2010

2010 17th Iranian Conference of Biomedical Engineering (ICBME)

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In this paper, a computer approach is proposed for recognition of retina layers on optical coherence tomography (OCT) images. OCT uses the optical backscattering of light to scan the eye and describe a pixel representation of the anatomic layers within the retina. Our approach is based on co-occurrence matrix for feature extraction and a supervised learning method for classification, which four features of this matrix have been used as a feature vector by support vector machine (SVM) has been used for segmentation retina layers. Achieved result of combined these two methods in the best state was 98.6% precision. This result shows that apply these methods on OCT images discriminate retina layers with efficient accuracy. Since, recognition of retina layers is important for automatic analyzing of OCT images, therefore our proposed methods can be very useful.

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Texture Classification of Retinal Layers in Optical Coherence Tomography

Cited by 16 publications

References 15 publications

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography

An image processing approach to automatic detection of retina layers using texture analysis

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