In Vivo Tumor Spatial Classification using PCA and K-Means with NIR-Hyperspectral Data


  • Mai Kasai Tokyo University of Science



This paper presents new method of spatial classification and wavelength bands reduction of near-inferred (NIR) hyperspectral imaging data for medical application. Hyperspectral imaging data have more than several hundred wavelength bands. However hyperspectral data have sometimes redundant information to detect region of interest. The aim of this research is to archive became possible that a region of interest is distinguished by observing the particular wavelength bands without any markers in order to develop a special application such as a surgery supporting system. NIR light with wavelengths of 800-2000 nm, called as the ‘biological window,’ has received particular attention given that water and biological tissues have minimal optical loss caused by scattering and absorption at these wavelengths. NIR light can penetrate/see through deep tissues. NIR endoscope have a great potential as the surgery supporting system, however  wavelength bands needs to reduce according to the limitation of NIR endoscope hardware performance. To consider only several wavelength bands are sometimes much effective case than to consider all wavelength bands. In this paper, we proposed the method of spatial classification and reduction a number of wavelength bands simultaneously by combined PCA and k-means, and assessed the cancer-caring nude mouse. The experimental results demonstrate that the proposed method can select valuable wavelength bands to distinguish the region of interest with comparable accuracy of the conventional method.

Author Biography

Mai Kasai, Tokyo University of Science

Department of Mechanical Engineering, Faculty of Science and Technology


. Schultz R. A., Nielsen T., Zavaleta J. R., Ruch R., Wyatt R., Ganner H. R., Hyperspectral Imaging: A Novel Approach For Microscopic Analyasis, Cytometry, Vol. 43, Issue 4, 2001, pp. 239-247.

. Guolan Lu, Baowei Fei, Medical hyperspectral imaging: a review, Journal of Biomedical Optics, Vol. 19(1), 2014, 010901.

. Guolan Lu, Luma Halig, Dongsheng Wang, Zhuo Georgia Chen, and Baowei Fei, Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images, Proc SPIE. NIH Public Access Author Manuscript, 2014, No. 92036, pp.1-11.

. Zako T., Ito M., Hyodo H., Yoshimoto M., Watanabe M., Takemura H., Kishimoto H., Kaneko K., Soga K., Maeda M., Extra-iluminal detection of assumed colonic tumor site by near-infrared laparoscopy, Surgical Endoscopy, 2015, pp. 1-7.

. Zako T., Hyodo H., Tsuji K., Tokuzen K., Kishimoto H., Ito M., Kaneko K., Maeda M. and Soga K., Development of near infrared-fluorescent nanophosphors and applications for cancer diagnosis and therapy, Journal of Nanomaterials, 2010 , Vol. 2010, pp.1-7.

. Amgren M., Hansen PW., Eriksen B., Larsen J., Larsen R., Analysis of Pregerminated Barley using Hyperspectral Image Analysis, Journal of Agricultural and Food Chemistrt, Vol. 59, 2011, pp. 11385-11394.

. Yaguchi A., Kobayashi T., Watanabe K., Iwata K., Hosaka T., Out N., Cancer Detection From Biopsy Images using Probabilistic and Discriminative Features, 2011 18th IEEE International Conference on Image Processing (ICIP), 2011, pp. 1609-1612.

. Serranti S., Cesare D., Marini F., Bonifazi G., Classification of oat and groat kernels using nir hyperspectral imaging, Talanta, Vol. 103, 2013, pp. 276-284.

. Okamoto, H., Murata, T., Kataoka, T. and Hata, S., Plant classification for weed detection using hyperspectral imaging with wavelet analysis, Weed Biology and Management, 2007, Vol. 7, pp.31-37.

. Jeng-Ren Duann, Chia-Ing Jan, Mnang Ou-Yang, Chia-Yi Lin, Jen-Feng Mo, Yung-Jiun Lin, Ming-Hsui Tsai, Jin-Chern Chiou, Separation spectral mixtures in hyperspectral image data using independent component analysis: validation with oral cancer tissue sections, Journal of Biomedical Optics, 2013, Vol. 18, No. 12, 126005.

. Naganathan G. K., Grimes L. M., Subbiah J., Calkins C. R., Samal A., Meyer G. E., Visible/Near-infrared Hyperspectral Imaging for Beef Tenderness Prediction, Computers and Electronics in Agriculture, Vol. 64, 2008, pp. 225-233.

. MacQueen J., Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1: Statistics, 1967, pp. 281-297.

. Wagstaff K., Cardie C., Rogers S., Schroedl S., Constrained K-means Clustering with Background Knowledge, Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 577-584.

. Komoriya K., Takemura H., Mizoguchi H., Soga K., Hyodo H., Kishimoto H., Kaneko K., NIR-fluorescent Imaging by Head-Scanning Mechanism for Near-Infrared Endoscope, Transaction of Japanese Society for Medical and Biomedical Engineering, Vol. 51, No. 2, 2013, pp.135-141.

. Peason K., On lines and planes of closest fit to systems of point in space, Philosophical Magazine, Vol. 2, 1901, pp.559-572.




How to Cite

Kasai, M. (2016). In Vivo Tumor Spatial Classification using PCA and K-Means with NIR-Hyperspectral Data. British Journal of Healthcare and Medical Research, 3(1), 45.