Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

Lingley-Papadopoulos, Colleen A.; Loew, Murray H.; Zara, Jason M.

doi:10.1117/1.3171943

Cited by 14 publications

(9 citation statements)

References 43 publications

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“…The wavelet transform has been widely used in OCT images for denoising and despeckling [149]–[151] as well as for texture analysis [152]. Early work on 3-D wavelet analysis of OCT images was reported in [148] and was based on a computationally efficient yet flexible non-separable lifting scheme in arbitrary dimensions [153].…”

Section: Oct Image Analysismentioning

confidence: 99%

Retinal Imaging and Image Analysis

Abràmoff

Garvin

Sonka

2010

IEEE Rev. Biomed. Eng.

1,196

714

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Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

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Section: Oct Image Analysismentioning

confidence: 99%

Retinal Imaging and Image Analysis

Abràmoff

Garvin

Sonka

2010

IEEE Rev. Biomed. Eng.

1,196

714

View full text Add to dashboard Cite

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“…The wavelet transform has also been applied to OCT images for texture analysis of tissues [32], where its effectiveness and its invariance to acquisition device have been shown. The fact that the wavelet transform is well suited to remove the speckle is a strong motivation to use it for tissue characterization [33].…”

Section: Automated Sead Footprint Detectionmentioning

confidence: 99%

Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula

Quellec

Lee

Dolejsi

et al. 2010

IEEE Trans. Med. Imaging

201

174

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Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions—SEADs (Symptomatic Exudate-Associated Derangements)—in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 ± 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and—together with an anatomical atlas of normal retinas—can be used for clinically important applications.

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“…Texture analysis has been used for diagnosis of dysplasia in Barrett's esophagus [96], for automated classification of gastrointenstinal tissues [97], and for differentiating between different human breast tissue types [98]. Texture analysis has also been combined with wavelets to improve classification performance by reducing the impact of system-dependent variations in the speckle pattern [99]. More recently, the fractal dimension, which is a measure of the self-similarity and complexity of the object, has been used to characterize texture.…”

Section: Analysis and Classificationmentioning

confidence: 99%