Medical Image Segmentation and Tracking Through the Maximisation or the Minimisation of Divergence Between PDFs

Abstract

In this chapter, we focus on statistical region-based active contour models where the region descriptor is chosen as the probability density function of an image feature (e.g. intensity) inside the region. Image features are then considered as random variables whose distribution may be either parametric, and then belongs to the exponential family, or non parametric and is then estimated through a Parzen window. In the proposed framework, we consider the optimization of divergences between such PDFs as a general tool for segmentation or tracking in medical images. The optimization is performed using a shape gradient descent through the evolution of an active region. Using shape derivative tools, our work is directed towards the construction of a general expression for the derivative of the energy (with respect to a domain), and the differentiation of the corresponding evolution speed for both parametric and non parametric PDFs. Experimental results on medical images (brain MRI, contrast echocardiography, perfusion MRI) confirm the availability of this general setting for medical structures segmentation or tracking in 2D or 3D.