Ανάπτυξη υπολογιστικών μεθόδων σε λογισμικό και υλικό για την ανάλυση βιοϊατρικών δεδομένων

Abstract

The topic of this thesis is the development of novel methods of biomedical data analysis in software and hardware. A novel system has been developed for the extraction of texture features from images and video. This system has been implemented in software, which is optimized for execution time, and hardware, which efficiently uses the features of the FPGA. The system calculates second order statistical features based on the gray level cooccurrence matrix. The hardware implementation is capable of calculating four 16-dimensional texture features, significantly reducing the necessary computational time, while preserving the accuracy of the results. The system has been evaluated on reference videos and achieves real time operation. A novel method for the calculation of the logarithm function has also been developed, which achieves fast operation, high accuracy and low resource utilization. It is based on a piecewise linear approximation using an arbitrarily high number of segments. Additionally, it provides adaptability to the input data set, which further enhances the accuracy. The evaluation of the hardware architecture was performed with regards to accuracy, speed of computation and required resources, whereas the experimental results illustrate its advantageous performance in comparison to state of the art methods. Finally, a novel method for automatic microarray image gridding has been developed. Microarray images contain a matrix of spots, which are separated by a grid based on the maximization of the margin between consecutive rows or columns of spots. This method employs image processing techniques to identify the spot positions and sizes, and subsequently selects the valid spots based on the spot size distribution. The valid spots are then provided as a training set to a number of linear support vector machine classifiers, which produce the grid lines. This method performs highly accurate microarray gridding on images that are affected by rotation, intense artifacts and spots of low or zero intensity. The evaluation was performed on reference microarray images, in comparison to state of the art methods