A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

Lan Chang; Mengmeng Zhang; Wei Li

doi:10.1117/12.2283229

24 October 2017 A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

Lan Chang, Mengmeng Zhang, Wei Li

Proceedings Volume 10461, AOPC 2017: Optical Spectroscopy and Imaging; 104610J (2017) https://doi.org/10.1117/12.2283229
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Citation Download Citation

Lan Chang, Mengmeng Zhang, and Wei Li "A coarse-to-fine approach for medical hyperspectral image classification with sparse representation", Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 104610J (24 October 2017); https://doi.org/10.1117/12.2283229

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