A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection

• Type of Material: Conference Paper
• Authors: Robert Pike, Emory University; Samuel K. Patton, Emory University; Guolan Lu, Georgia Institute of Technology; Luma V. Halig, Emory University; Dongsheng Wang, Emory University; Georgia Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2014-01-01
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2043848
• Title of Journal or Parent Work: MEDICAL IMAGING 2014: IMAGE PROCESSING
• Conference or Event Name: Conference on Medical Imaging - Image Processing
• Volume: 9034
• Start Page: 90341W
• End Page: 90341W
• Grant/Funding Information: This research is supported in part by NIH grants (R01CA156775, R21CA176684, and P50CA128301) and Georgia Cancer Coalition Distinguished Clinicians and Scientists Award.
• Keywords: image classification; SEGMENTATION; support vector machine; Optics; REGISTRATION; SUPPORT VECTOR MACHINES; minimum spanning forest; MR-IMAGES; Hyperspectral imaging; MR/PET; Radiology, Nuclear Medicine & Medical Imaging; Imaging Science & Photographic Technology; ATTENUATION CORRECTION; Physical Sciences; Life Sciences & Biomedicine; MULTISCALE; Technology; Science & Technology
• Research Categories: Mathematics

Abstract

Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images. © 2014 SPIE.