To segment brain tissues in magnetic resonance images of the brain, the authors have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation the authors have extended their method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying the authors' stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.
Developers of new algorithms typically require an interactive environment in which the image data can be passed through various operators, some of which may involve feedback, synchronization, merging and conditional control strategies. This paper describes how the dataflow methodology of a pictorial object-oriented software development tool called WIT has greatly simplified the prototyping and testing of our new image registration and segmentation methods. WIT allows the user to draw a dataflow graph by linking operators in a CAD-like manner. We describe the main features of WIT, and show dataflow graphs for two medical image analysis algorithms; the 3D registration of P E T scans into a common coordinate space, and tissue segmentation in MRI images where we are looking at quantitation of tumour volumes. tals between operators that allow the data to flow through the igraph. Figure 1 contains a simple igraph utilizing the if control operator. Images A and B enter into independent stats operators that calculate the mean ( E ) , standard deviation (o), floor (1 J), and ceiling (1 1) of the pixel values. The standard deviation portal from each operator is A ? 6 condition' ?
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