Medical image analysis is performed in order to facilitate medical research and ultimately provide better healthcare. It is critical to the advancement of imaging-based medical research, for example, using magnetic resonance (MR) imaging to probe brain structural and functional changes related to a disease or cognitive process. This special issue highlights new methods of signal and image proce...

Image data is of immense practical importance in medical informatics. Medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), Ultrasound, and X-Ray, in standard DICOM formats are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information in EHR clinical management systems. Research efforts have been devoted to...

We propose a novel approach to landmark-based medical image registration based on the geostatical method of Kriging prediction. Our method exploits the spatial statistical relation between two images, as estimated using generalpurpose registration algorithms, in order to construct an optimum predictor of the displacement field. High accuracy is achieved by using an estimated spatial model of th...

Three Bayesian ideas are presented for supervised adaptive classiers. First, it is argued that the output of a classier should be obtained by marginalising over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a `moderation' of the most probable classi-er's outputs, and yields improved performance. Second, it is de...

A self-organizing neural network model for mechanism of pattern recognition unaected by shift in position. of Japanese Kanji using principal component analysis as a preprocessor to an articial neural etwork. 10 necessary, only one initial guess was used. These results show that for character classication accuracy NN methods and statistical methods have comparable accuracies conrming the COCR re...

in the last two decades, the trend in healthcare information technology has been towards digital and multimedia concepts. the next generation of healthcare information systems will consist of a vast network of heterogeneous, autonomous and distributed imaging scanners, hospital information systems (his), medical information from clinical departments such as radiology information systems (ris), ...

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...