10591

Multi-Scale, Multi-Level, Heterogeneous Features Extraction and Classification of Volumetric Medical Images

Shuai Li, Qinping Zhao, Shengfa Wang, Aimin Hao, Hong Qin
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China
2013 IEEE International Conference on Image Processing, 2013

@article{li2013multi,

   title={Multi-Scale, Multi-Level, Heterogeneous Features Extraction and Classification of Volumetric Medical Images},

   author={Li, Shuai and Zhao, Qinping and Wang, Shengfa and Hao, Aimin and Qin, Hong},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

1574

views

This paper articulates a novel method for the heterogeneous feature extraction and classification directly on volumetric images, which covers multi-scale point feature, multi-scale surface feature, multi-level curve feature, and blob feature. To tackle the challenge of complex volumetric inner structure and diverse feature forms, our technical solution hinges upon the integrated approach of locally-defined diffusion tensor (DT), DT-based anisotropic convolution kernel (DACK), DACK-based multi-scale analysis, and DT-governed curve feature growing. The extracted structural features can be further semantically classified. At the computational fronts, we design CUDA-based algorithm to conduct parallel computation for time consuming tasks. Various experiments and timing tests demonstrate the effectiveness, robustness, and high performance of our method.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: