Optimal feature selection applied to multispectral fluorescence imaging

TY - CHAP

T1 - Optimal feature selection applied to multispectral fluorescence imaging

AU - Wood, Tobias C

AU - Thiemjarus, Surapa

AU - Koh, Kevin R

AU - Elson, Daniel S

AU - Yang, Guang-Zhong

PY - 2008

Y1 - 2008

N2 - Recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo--in situ application. This offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. One of the key requirements for in vivo consideration is to minimise the acquisition window to avoid tissue motion and deformation, whilst making the best use of the available photons to account for correlation or redundancy between different dimensions. The purpose of this paper is to propose a feature selection framework to identify the best combination of features for discriminating between different tissue classes such that redundant or irrelevant information can be avoided during data acquisition. The method is based on a Bayesian framework for feature selection by using the receiver operating characteristic curves to determine the most pertinent data to capture. This represents a general technique that can be applied to different multi-modal imaging modalities and initial results derived from phantom and ex vivo tissue experiments demonstrate the potential clinical value of the technique.

AB - Recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo--in situ application. This offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. One of the key requirements for in vivo consideration is to minimise the acquisition window to avoid tissue motion and deformation, whilst making the best use of the available photons to account for correlation or redundancy between different dimensions. The purpose of this paper is to propose a feature selection framework to identify the best combination of features for discriminating between different tissue classes such that redundant or irrelevant information can be avoided during data acquisition. The method is based on a Bayesian framework for feature selection by using the receiver operating characteristic curves to determine the most pertinent data to capture. This represents a general technique that can be applied to different multi-modal imaging modalities and initial results derived from phantom and ex vivo tissue experiments demonstrate the potential clinical value of the technique.

U2 - 10.1007/978-3-540-85990-1_27

DO - 10.1007/978-3-540-85990-1_27

M3 - Conference paper

C2 - 18982609

VL - 11

T3 - Lecture Notes in Computer Science

SP - 222

EP - 229

BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

PB - Springer

ER -