A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

Robert Pike, Samuel K. Patton, Guolan Lu, Luma V. Halig, Dongsheng Wang, Zhuo Georgia Chen, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
PublisherSPIE
ISBN (Print)9780819498274
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: Feb 16 2014Feb 18 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9034
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2014: Image Processing
CountryUnited States
CitySan Diego, CA
Period2/16/142/18/14

Fingerprint

image classification
Image classification
Tissue
Bearings (structural)
cancer
Neoplasms
Support vector machines
mice
animals
Tumors
Animals
tumors
examination
Imaging techniques
Experiments

Keywords

  • Hyperspectral imaging
  • Image classification
  • Minimum spanning forest
  • Support vector machine

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Pike, R., Patton, S. K., Lu, G., Halig, L. V., Wang, D., Chen, Z. G., & Fei, B. (2014). A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. In Medical Imaging 2014: Image Processing [90341W] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9034). SPIE. https://doi.org/10.1117/12.2043848

A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. / Pike, Robert; Patton, Samuel K.; Lu, Guolan; Halig, Luma V.; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei.

Medical Imaging 2014: Image Processing. SPIE, 2014. 90341W (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9034).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pike, R, Patton, SK, Lu, G, Halig, LV, Wang, D, Chen, ZG & Fei, B 2014, A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. in Medical Imaging 2014: Image Processing., 90341W, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9034, SPIE, Medical Imaging 2014: Image Processing, San Diego, CA, United States, 2/16/14. https://doi.org/10.1117/12.2043848
Pike R, Patton SK, Lu G, Halig LV, Wang D, Chen ZG et al. A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. In Medical Imaging 2014: Image Processing. SPIE. 2014. 90341W. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2043848
Pike, Robert ; Patton, Samuel K. ; Lu, Guolan ; Halig, Luma V. ; Wang, Dongsheng ; Chen, Zhuo Georgia ; Fei, Baowei. / A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. Medical Imaging 2014: Image Processing. SPIE, 2014. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{273ba9bed5b748ae84f043c6b1ead829,
title = "A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection",
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.",
keywords = "Hyperspectral imaging, Image classification, Minimum spanning forest, Support vector machine",
author = "Robert Pike and Patton, {Samuel K.} and Guolan Lu and Halig, {Luma V.} and Dongsheng Wang and Chen, {Zhuo Georgia} and Baowei Fei",
year = "2014",
month = "1",
day = "1",
doi = "10.1117/12.2043848",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",

}

TY - GEN

T1 - A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

AU - Pike, Robert

AU - Patton, Samuel K.

AU - Lu, Guolan

AU - Halig, Luma V.

AU - Wang, Dongsheng

AU - Chen, Zhuo Georgia

AU - Fei, Baowei

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - Hyperspectral imaging

KW - Image classification

KW - Minimum spanning forest

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=84902094482&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902094482&partnerID=8YFLogxK

U2 - 10.1117/12.2043848

DO - 10.1117/12.2043848

M3 - Conference contribution

C2 - 25426272

AN - SCOPUS:84902094482

SN - 9780819498274

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2014

PB - SPIE

ER -