3D Ultrasound Image Segmentation: Interactive Texture-Based Approaches

Olivier, Julien; Paulhac, Ludovic

doi:10.5772/35934

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…They applied expectation maximization algorithm to calculate statistical distribution of each object classes to distinguish each region voxels from neighborhoods, then applied watershed transform to corrects the segmentation errors. Olivier et al [130] presented texture-based methods for skin segmentation from 3D US data using a multi resolution scheme for volumetric texture and an supervised binary classifier with manual initialization.…”

Section: Miscellaneous Clinical Purposesmentioning

confidence: 99%

3D Ultrasound image segmentation: A Survey

Mozaffari,

Lee

2016

Preprint

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Three-dimensional Ultrasound image segmentation methods are surveyed in this paper. The focus of this report is to investigate applications of these techniques and a review of the original ideas and concepts. Although many two-dimensional image segmentation in the literature have been considered as a threedimensional approach by mistake but we review them as a three-dimensional technique. We select the studies that have addressed the problem of medical threedimensional Ultrasound image segmentation utilizing their proposed techniques. The evaluation methods and comparison between them are presented and tabulated in terms of evaluation techniques, interactivity, and robustness.

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Section: Miscellaneous Clinical Purposesmentioning

confidence: 99%

3D Ultrasound image segmentation: A Survey

Mozaffari,

Lee

2016

Preprint

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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)mentioning

confidence: 99%

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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Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

Benabdallah

Djerou²

2022

Arab J Sci Eng

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3D Ultrasound Image Segmentation: Interactive Texture-Based Approaches

Cited by 4 publications

References 42 publications

3D Ultrasound image segmentation: A Survey

3D Ultrasound image segmentation: A Survey

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

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