Parallel Implementation of Systolic Array Design for Developing Medical Image Rotation

Many image-processing algorithms are particularly suited to parallel computing, as they process images that are difficult and time consuming to analyse. In particular, medical images of tissues tend to be very complex with great irregularity and variability in shapes. Furthermore, existing algorithms contain explicit parallelism, which can be efficiently exploited by processing arrays. A good example of an image processing operation is the geometric rotation of a rectangular bitmap. This paper presents a set of systolic array designs for implementing the geometric rotation algorithms of images on VLSI processing arrays. The examined algorithm performs a trigonometric transformation on each pixel in an image.  The design is implemented as a distributed computing system of networked computers using Parallel Virtual Machine (PVM) model. Each node (computer) in the network takes part in the task in hand – such as image processing – using message passing. Comments and conclusions about the implementation of the design as a distributed computing system are discussed. Keywords: parallel computing, distributed computing. PVM, image rotation, systolic array

Image Content Analysis Using Neural Networks and Genetic Algorithms

The analysis of digital images for content discovery is a process of identifying and classifying patterns and sub-images that can lead to recognizing contents of the processed image. The image content analysis system presented in this paper aims to provide the machine with the capability to simulate in some sense, a similar capability in human beings. The developed system consists of three levels. In the low level, image clustering is performed to extract features of the input data and to reduce dimensionality of the feature space. Classification of the scene images are carried out using a single layer neural network, trained through Kohonen's self-organizing algorithm, with conscience function, to produce a set of equi-probable weights vector. The intermediate level consists of two parts. In the first part an image is partitioned into homogeneous regions with respect to the connectivity property between pixels, which is an important concept used in establishing boundaries of objects and component regions in an image. For each component, connected components can be determined by a process of component labeling. In the second part, feature extraction process is performed to capture significant properties of objects present in the image. In the high level; extracted features and relations of each region in the image are matched against the stored object models using the genetic algorithm approach. The implemented system is used in the analysis and recognition of colored images that represent natural scenes. Keywords: genetic algorithms, neural networks, image segmentation, clustering, image content analysis