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IEEE Trans Biomed Eng. 2016 Mar;63(3):653-63. doi: 10.1109/TBME.2015.2468578. Epub 2015 Aug 14.

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.

Abstract

GOAL:

The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model.

METHODS:

An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information.

CONCLUSION:

The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images.

SIGNIFICANCE:

Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.

PMID:
26285052
PMCID:
PMC4791052
[Available on 2017-03-01]
DOI:
10.1109/TBME.2015.2468578
[Indexed for MEDLINE]
Free PMC Article

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