TY - GEN
T1 - Spatial-spectral blood cell classification with microscopic hyperspectral imagery
AU - Ran, Qiong
AU - Chang, Lan
AU - Li, Wei
AU - Xu, Xiaofeng
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2017
Y1 - 2017
N2 - Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.
AB - Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.
KW - Blood Cell Classification
KW - Extreme Learning Machine
KW - Microscpic Hyperspectral Image
KW - Spatial-Spectral Classification
UR - http://www.scopus.com/inward/record.url?scp=85040768612&partnerID=8YFLogxK
U2 - 10.1117/12.2281268
DO - 10.1117/12.2281268
M3 - Conference contribution
AN - SCOPUS:85040768612
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - AOPC 2017
A2 - Hang, Wei
A2 - Hou, Xiandeng
A2 - Zhao, Bing
A2 - Wang, Zhe
A2 - Xie, Mengxia
A2 - Shimura, Tsutomu
A2 - Yu, Jin
PB - SPIE
T2 - Applied Optics and Photonics China: Optical Spectroscopy and Imaging, AOPC 2017
Y2 - 4 June 2017 through 6 June 2017
ER -