Hierarchical clustering using equivalence test: Application on automatic segmentation of Dynamic Contrast-Enhanced image sequence.
vendredi 20 janvier 2017, 9h30 - 10h30
Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarker for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in large regions of interest, which however need to be functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions. Up to a modeling which depends on one parameter a and is justified a posteriori, the proposed method is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to preserve the local property of anatomical features, and the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given an expected homogeneity discrepancy δ for the multiple equivalence test, both steps stop automatically through a control of the Type I error, providing an adaptive choice of the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with tissue well separated in term of temporal signals, the method is proven to be able to retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. Parameter δ appears as the tuning parameter controlling the size and the complexity of the segmentation.