An autonomic sensing framework for body sensor networks
Proceedings of the ICST 2nd international conference on Body area networks
Tissue characterization using dimensionality reduction and fluorescence imaging
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
An eye-hand data fusion framework for pervasive sensing of surgical activities
Pattern Recognition
| Hi-index | 0.00 |
Recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo - in situapplication. This offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. One of the key requirements for in vivoconsideration 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 vivotissue experiments demonstrate the potential clinical value of the technique.