ArticleMedical physics2012
3D ultrasound image segmentation using wavelet support vector machines.
Article in Medical physics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Review of Linear-Array-Transducer-Based Volumetric Ultrasound Imaging Techniques and Their Biomedical Applications.Bioengineering (Basel, Switzerland) · 2025Review
- Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
- Bibliometric Analysis of Global Research Trends on Ultrasound Microbubble: A Quickly Developing Field.Frontiers in pharmacology · 2021Article
- Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.International journal of computer assisted radiology and surgery · 2018Article
- PSNet: prostate segmentation on MRI based on a convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- A random walk-based segmentation framework for 3D ultrasound images of the prostate.Medical physics · 2017Article
- Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Molecular imaging and fusion targeted biopsy of the prostate.Clinical and translational imaging · 2017Article
- Deep Learning based Classification for Head and Neck Cancer Detection with Hyperspectral Imaging in an Animal Model.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- A supervoxel-based segmentation method for prostate MR images.Medical physics · 2017Article
- Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.Academic radiology · 2016Review
- Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges.Medical physics · 2016Article
- Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Simulating cardiac ultrasound image based on MR diffusion tensor imaging.Medical physics · 2015Article
- Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions.Physics in medicine and biology · 2014Article
- A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
purposeTransrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy.
methodsThis segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method.
resultsThe results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%.
conclusionsThe proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.
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