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Volumetric texture modeling using dominant and discriminative binary patterns
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Paper Abstract

Volumetric texture analysis is an import task in medical imaging domain and is widely used for characterizing tissues and tumors in medical volumes. Local binary pattern (LBP) based texture descriptors are quite successful for characterizing texture information in 2D images. Unfortunately, the number of binary patterns grows exponentially with number of bits in LBP. Hence its straightforward extension to 3D domain results in extremely large number of bit patterns that may not be relevant for subsequent tasks like classification. In this work we present an efficient extension of LBP for 3D data using decision tree. The leaves of this tree represent texture words whose binary patterns are encoded using the path being followed from the root to reach the leaf. Once trained, this tree is used to create histogram in bag-of-words fashion that can be used as texture descriptor for whole volumetric image. For training, each voxel is converted into a 3D LBP pattern and is assigned the label of it’s corresponding volumetric image. These patterns are used in supervised fashion to construct decision tree. The leaves of the corresponding tree are used as texture descriptor for downstream learning tasks. The proposed texture descriptor achieved state of the art classification results on RFAI database 1. We further showed its efficacy on MR knee protocol classification task where we obtained near perfect results. The proposed algorithm is extremely efficient, computing texture descriptor of typical MRI image in less than 100 milliseconds.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490H (15 March 2019); doi: 10.1117/12.2512296
Show Author Affiliations
Parmeet S. Bhatia, Siemens Healthineers (United States)
Amit Kale, Bosch Corporate Research and Technology Ctr. (India)
Zhigang Peng, Siemens Healthineers (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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