Human Understandable Features for Segmentation of Solid Texture

Paulhac, Ludovic; Makris, Pascal; Grégoire, Jean‐Marc; Ramel, Jean-Yves

doi:10.1007/978-3-642-10331-5_36

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Analysis of 3-D connected components in binarized images (Martín-Herrero, 2007;Toriwaki and Yoshida, 2009;Sandfort and Ohser, 2009) was used for 3-D medical texture characterization in Paulhac et al (2009b,a), Paulhac (2009), andPaulhac et al (2009c). The success of the 3-D connected component approach relies strongly on the critical binarization step that is highly sensitive to illumination differences between datasets.…”

Section: Connected Components Blobs and Watershedssupporting

confidence: 41%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

201

146

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a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

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Section: Connected Components Blobs and Watershedssupporting

confidence: 41%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

201

146

View full text Add to dashboard Cite

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“…For the segmentation task, the database was used to evaluate several state-of-the-art descriptors in [47] and [48] and to design new features capable to encode the human perception of texture [49]- [51]. Suzuki et al used the database to evaluate a texture retrieval technique developed previously [52], [53], as well as a novel key-point detector [54].…”

Section: D Solid Texture (3dst)supporting

confidence: 43%

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Cid

Müller

Platon

et al. 2017

IEEE Trans. on Image Process.

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Abstract-Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3D content. One of the most common methods to analyze 3D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3D. Current state-of-the-art techniques face many challenges when working with 3D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3D Riesz-wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3D Rieszwavelet transforms. The estimations of local texture orientations are based on higher-order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods achieved an accuracy of 0.95 in the rotated data, three times more than the unaligned Riesz descriptor that achieved 0.32. The accuracy obtained is better than all other techniques that are published and tested on the same database.Index Terms-3D solid texture analysis, 3D texture classification, Riesz-wavelets steerability, aligned higher-order Riesz-wavelet transform, rotation-covariance.

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“…In order to evaluate the proposed feature extraction technique, we used the RFAI dataset [31] which is publicly available. It is composed of volumetric synthetic textures of size 64 × 64 × 64 voxels.…”

Section: Datasetmentioning

confidence: 99%

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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Human Understandable Features for Segmentation of Solid Texture

Cited by 7 publications

References 18 publications

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

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