Medical image analysis

Medical Image Analysis Foreword This special issue of Medical Image Analysis includes a selection of papers presented at the First UK Medical Image Understanding and Analysis meeting (MIUA97), held in Oxford, England in July 1997. A key aim of the event was to encourage the growth of this important multidisciplinary field by bringing together the diverse communities of researchers involved in different aspects of medical image understanding and analysis. A healthy total of 32 groups from the UK and Europe were represented at the meeting. The programme consisted of 26 oral and 27 poster presentations held over a two-day period. The three papers presented in this issue were selected by the technical programme committee as a sample representative of the high quality of exciting work being undertaken in this emerging discipline. Each paper was expanded by the authors before submission to the journal, reviewed by three anonymous reviewers and finally accepted after any necessary revision. It is interesting that all three deal with the problem of using prior knowledge of biological structure, though in different ways. The first paper by Zwiggelaar et al. describes recent work on computer-aided mammography. Spiculated lesions are detected with high sensitivity and specificity using a modelbased approach. Generic representations of blob-like features and of arrangements of linear structures are used, in conjunction with statistical modelling, to learn the appearance of tumours from a training set of known abnormalities. The two remaining papers both deal with image-based methods for diagnosing cardiac disease. Jacob et al. describe a novel method for tracking cardiac chamber boundaries in 2D ultrasound (B-mode) image sequences based on spatio-temporal (‘dynamic contour’) techniques aimed at quantifying heart dynamics. The approach involves constraining the way a contour can deform

between frames so it belongs to a set of allowable shapes defined by a ‘shape-space’ model and is consistent with the evidence provided by image measurements (the classic Kalman filter problem). The shape-space is learnt from a training set of images. The authors compare a number of different tracking models on real image sequences and show that tracking performance can be considerably improved if conventional derivative-based boundary localisation is replaced by a phase-based method that is invariant to intensity and thus less sensitive to imaging conditions. The paper by Sanchez-Ortiz et al. is concerned with the segmentation of cardiac cine MR images. Their new contribution is to develop an interesting adaptation of anisotropic diffusion, which incorporates a geometric model into the conductance function to enable the diffusion process to be spatially as well as intensity dependent. The method is applied to both anatomical and velocity encoded cardiac MR image sequences. MIUA97 was sponsored by the British Machine Vision Association, the British Institute of Radiology, the Institute of Electrical Engineers, the Institute of Physics in Engineering and Medicine, and the Royal Academy of Engineering. The on-line version of the full Proceedings of MIUA97 is available at the web site http:/www/robots.ox.ac.uk/~mvl/miua97.html. We thank the programme committee, presenters and participants for contributing to the success of MIUA97 and are grateful to Nicholas Ayache and Jim Duncan for inviting us to put together this special issue as a taster of the current state-ofart in medical image analysis in the UK. Chris Taylor, University of Manchester, UK Alison Noble, University of Oxford, UK December 1998