Fundamentals of Texture Processing for Biomedical Image Analysis

Depeursinge, Adrien; Fageot, Julien; Al-Kadi, Omar S.

doi:10.1016/b978-0-12-812133-7.00001-6

Cited by 27 publications

(36 citation statements)

References 42 publications

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“…Biomedical texture operations analyse the desired texture information in terms of spatial scales and the directions of a texture image, which are fundamental for visual texture discrimination. The most important characteristic of texture is that it is scale dependent [80]. Due to the fact that pixel-based grey-level-only methods are not sufficient to discriminate the complicated structures of tissue components, texture features are used [81].…”

Section: Texture-based Featuresmentioning

confidence: 99%

“…Low-level quantitative image analysis can be categorised based on intensity and texture. Image intensity describes the statistical distribution of the pixel values inside a Region of Interest (ROI) [80]. Many forms of texture analysis have been developed, including co-occurrence matrices, second-order statistics, Gauss-Markov random field, etc.…”

Section: Texture-based Featuresmentioning

confidence: 99%

“…These methods were limited to the analysis of spatial relations within small neighbourhoods and the analysis of micro textures [16]. However, texture information relates to the micro-and macro-structural context of biomedical tissue [80]. Classic examples of texture operators such as Gabor wavelets, GLCMs, Histogram of Oriented Gradients (HOG), LBPs, and Scattering Transforms (ST) need to be able to discriminate between types of image scales, directions, and transitions.…”

Section: Texture-based Featuresmentioning

confidence: 99%

“…Therefore, combinations of image scales and directions with discriminative capabilities for each plaque component should be considered. If the descriptor is insensitive to the transformation of the input image, the operator is invariant [80].…”

Section: Texture-based Featuresmentioning

confidence: 99%

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Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

et al. 2018

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Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

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Section: Texture-based Featuresmentioning

confidence: 99%

Section: Texture-based Featuresmentioning

confidence: 99%

Section: Texture-based Featuresmentioning

confidence: 99%

Section: Texture-based Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

et al. 2018

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“…Another fundamental and general aspect that needs to be accounted for is that the same texture pattern can appear at several local orientations. Features that are locally rotation-invariant are desirable in such instances (Depeursinge et al, 2017b;Schmid, 2001). LBPs (Ojala et al, 2002) and Rotation-covariant SIFT (RIFT) (Lazebnik et al, 2005) possess such a property, but they do not model discriminative patterns specifically (i.e., they yield handcrafted representations) and require exhaustive calculations.…”

Section: Introductionmentioning

confidence: 99%

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

Joyseeree

Otálora

Müller

et al. 2019

Medical Image Analysis

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A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatures are learned from Riesz representations using a one-versus-one approach. The signatures are generated for four diseased tissue types and a healthy tissue class, all of which frequently appear in the publicly available Interstitial Lung Diseases (ILD) dataset used in this article.

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