The Center for Biomedical Informatics
State University of Campinas, Brazil

Research Abstracts

Artificial neural networks (ANN) have demonstrated many advantages and capabilities in applications involving the processing of biomedical images and signals. Particularly in the field of medical image processing, ANNs have been used in several ways, such as in image filtering, scatter correction, edge detection, segmentation, pattern and texture classification, image reconstruction and alignment, etc. The adaptive nature of ANNs (i.e., they are capable of learning) and the possibility of implementing its function using truly massive parallel processors and neural integrated circuits, in the future; are strong arguments in favor of investigating new architectures, algorithms and applications for ANNs in Medicine. In the present work, we are interested into designing a prototype ANN which could be capable of processing serial sections of the brain, obtained from CT or MRI tomographs. The segmented, outlined images, representing internal brain structures, both normal and abnormal, would then be used as an input to a three-dimensional stereotaxic radiosurgery planning software. The ANN-based algorithm we have devised was initially implemented as a software simulation in a microcomputer (PC 80386, with VGA color graphics and a 80387 mathematical coprocessor). It is structured as a compound ANN, comprised by three cascading sub-networks. The first one receives the original digitized image, and is a one-layer, fully interconnected ANN, with one processing element (PE) per image pixel. The brain image is obtained from a General Electric CT system, with 256 x 256 pixels and 256 gray levels. The first ANN implements a MHF lateral inhibition function, based on a convolution filter of variable dimension (3 x 3 up to 9 x 9 PE's), and it is used to iteratively enhance borders in the image. The PE interconnection (i.e. convolution) function can be defined by the user as a disk file containing a set of synaptic weights, which is read by the program; thus allowing for experimentation with different sets of coefficients and sizes of the convolution window. In this layer, PE's have synaptic weights varying from -1 to 1, and the step function as its transfer function. Usually after 2 to 3 iterations, the borders are completely formed and do not vary any more, but are too thick (i.e., the trace width spans several pixels). In order to thin out the borders, the output of the MHF ANN layer is subsequently fed into a three-layer perceptron, which was trained off-line using the backpropagation algorithm to perform thinning on smaller straight line segments. Finally, the thinned out image obtained pixel-wise at the this ANN's output is fed into a third network, also a three-layer perceptron trained off-line using the backpropagation algorithm to complete small gaps ocurring in the image contours. The final image, also 256 x 256 pixels with 2 levels of gray, is passed to the 3D slice reconstruction program, implemented with conventional, sequential algorithms. A fourth ANN perceptron previously trained by back-propagation to recognize the gray histogram signature of small groups of pixels in the original image (such as bone, liquor, gray and white matter, blood, dense tumor areas, etc.), is used to false-color the entire image according to the classified thematic regions. The cascaded, multilayer ANN thus implemented performs very well in the overall task of obtaining automatically outlined and segmented brain slices, for the purposes of 3D reconstruction and surgical planning. Due to the complexity of algorithms and to the size of the image, the time spent by the computer we use is inordinately large, preventing a practical application. We are now studying the implementation of this ANN paradigm in RISC-based and vector-processing CPUs, as well as the potential applications of neurochip prototyping kits already available in the market.

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Last Updated: March 2, 1996

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