Image enhancement based on in vivo hyperspectral gastroscopic images: a case study

Abstract. Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications.

[1]  J. Kybic.  ELASTIC IMAGE REGISTRATION USING PARAMETRIC DEFORMATION MODELS , 2001 .

[2]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[3]  Lihong V. Wang.  Photoacoustic imaging and spectroscopy , 2009 .

[4]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[5]  N. N. Murthy,et al.  Elastic, maximal matching , 1991, Pattern Recognit..

[6]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[7]  Daniel L. Farkas,et al.  The hyperspectral imaging endoscope: a new tool for in vivo cancer detection , 2004, SPIE BiOS.

[8]  Luma V. Halig,et al.  Hyperspectral imaging and quantitative analysis for prostate cancer detection. , 2012, Journal of biomedical optics.

[9]  Yoshihiko Hamamoto,et al.  New method for detection of gastric cancer by hyperspectral imaging: a pilot study , 2013, Journal of biomedical optics.

[10]  N. Rajpoot,et al.  Hyperspectral Colon Tissue Classification using Morphological Analysis , 2006, 2006 International Conference on Emerging Technologies.

[11]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[12]  V. Tuchin.  Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis , 2000 .

[13]  Holger Lange,et al.  Reflectance and fluorescence hyperspectral elastic image registration , 2004, SPIE Medical Imaging.

[14]  Robert P. W. Duin,et al.  Multi-spectral video endoscopy system for the detection of cancerous tissue , 2013, Pattern Recognit. Lett..

[15]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[16]  Filiberto Pla,et al.  Unsupervised band selection for multispectral images using information theory , 2004, ICPR 2004.

[17]  Tuan Vo-Dinh,et al.  Development of an Advanced Hyperspectral Imaging (HSI) System with Applications for Cancer Detection , 2006, Annals of Biomedical Engineering.

[18]  Guang-Zhong Yang,et al.  Multispectral image alignment using a three channel endoscope in vivo during minimally invasive surgery , 2012, Biomedical optics express.

[19]  Jörg Bendix,et al.  Hyperspectral imaging of mucosal surfaces in patients , 2012, Journal of biophotonics.

[20]  W. Wolfe.  Introduction to Imaging Spectrometers , 1997 .

[21]  Boris Thies,et al.  Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx , 2012, International Journal of Health Geographics.

[22]  Xiguang Wang,et al.  In vivo use of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors , 2016, Journal of biomedical optics.

[23]  Vijayashree S. Bhattar,et al.  Accuracy of In Vivo Multimodal Optical Imaging for Detection of Oral Neoplasia , 2012, Cancer Prevention Research.