TY - GEN
T1 - A new segmentation framework for infrared spectroscopic imaging using frequent pattern mining
AU - Kwak, Jin Tae
AU - Sinha, Saurabh
AU - Bhargava, Rohit
PY - 2011
Y1 - 2011
N2 - Histologic analysis of a stained tissue sample by a trained pathologist forms the definitive diagnosis of prostate cancer. Rapid and objective second opinions are highly desirable to make more accurate diagnostic decisions. One alternate method is to use Fourier transform infrared (FT-IR) spectroscopic imaging, which is an emerging technique that combines the molecular selectivity of spectroscopy with the spatial specificity of optical microscopy. While instrumentation is well-developed for FT-IR imaging, information extraction from the data could benefit greatly from improved approaches. Here we propose a new approach to segment histologic classes in a tissue for FT-IR imaging using frequent pattern mining. Prior to applying frequent pattern mining, FT-IR images are discretized, and subsequent pruning method and feature selection method result in a classifier for the segmentation. The method is evaluated using two different datasets. Results indicate that accurate histologic segmentation is achievable by this approach.
AB - Histologic analysis of a stained tissue sample by a trained pathologist forms the definitive diagnosis of prostate cancer. Rapid and objective second opinions are highly desirable to make more accurate diagnostic decisions. One alternate method is to use Fourier transform infrared (FT-IR) spectroscopic imaging, which is an emerging technique that combines the molecular selectivity of spectroscopy with the spatial specificity of optical microscopy. While instrumentation is well-developed for FT-IR imaging, information extraction from the data could benefit greatly from improved approaches. Here we propose a new approach to segment histologic classes in a tissue for FT-IR imaging using frequent pattern mining. Prior to applying frequent pattern mining, FT-IR images are discretized, and subsequent pruning method and feature selection method result in a classifier for the segmentation. The method is evaluated using two different datasets. Results indicate that accurate histologic segmentation is achievable by this approach.
KW - discretization
KW - feature selection
KW - frequent pattern mining
KW - histological segmentation
KW - Infrared spectroscopic imaging
UR - http://www.scopus.com/inward/record.url?scp=80055051143&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80055051143&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2011.5872443
DO - 10.1109/ISBI.2011.5872443
M3 - Conference contribution
AN - SCOPUS:80055051143
SN - 9781424441280
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 452
EP - 455
BT - 2011 8th IEEE International Symposium on Biomedical Imaging
T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Y2 - 30 March 2011 through 2 April 2011
ER -