Publication: Dsp-based pseudo colouring system for medical images
Loading...
Date
2009-04-01
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Before the advancement of digital image processing, medical images were analyze in analogue where the images were printed on films. The images were mostly in black and white and were hard to be analyzed because human eyes cannot differentiate greyscale image as good as the colour images. When digital image processing was first used to analyze medical images, the system was bulky and often consumed large space. Thus, this project was undertaken to focus on the digital image processing for the medical images. Pseudocolouring algorithm was developed and applied to the medical images for easier analysis by medical doctors. Two pseudo-colouring algorithms were developed which were the conventional method (i.e. the user needs to input the threshold value manually) and the second algorithm was the automatic threshold adjustment (i.e. the threshold value was automatically calculated for the images). The automatic pseudo-colouring algorithm used two clustering algorithms which were the K-Mean and the Moving K-Mean algorithms. The clustering algorithm clustered the images into several clusters before the pseudo-colouring algorithm was applied to the images. After the pseudocolouring algorithm was created, the algorithm was implemented onto the TMS320C6416T Digital Signal Processor so that it can be mobile and portable without the needs of a personal computer. When the implementation was completed, the TMS320C6416T target board was then optimized such that it can operate or process the image at its optimum performance. The performance of the TMS320C6416T was based on the execution time taken for it to process the medical images. Higher performances mean that the time required to process the images was shorter. The optimization included compiler's optimization options and also the structure of the algorithm being created. After the algorithm was implemented and optimized on the
TMS320C6416T, several medical images were tested using the developed system. Results from the developed system showed that Moving K-Mean algorithm was more suitable for automatic pseudo-colouring. The clustering process was more efficient where it can cluster better than the K-Mean. Optimization was proven that it can increase the performance of the TMS320C6416T processing. The execution time after the optimization process was reduced by half. Other than that, three medical images which were the mammogram, pap-smear and rat sperm images were successfully converted to its equivalent pseudo-colour images using three level of pseudo-colour algorithm designed specifically for each images. From the result, it was proven that the research was successfully carried-out and the developed system can convert medical images to its equivalent pseudo-colour images.