Application of Circular Hough Transform on cDNA Microarray Analysis

In this research, we used circular Hough transform to be the core method to search circular spots in microarray images. Due to high sensitivity of circular Hough transform to noises in an image, noise removal plays a very important role in image pre-processing. At first, we used histogram equalization to enhance the contrast of images before image thresholding. As a result, it is easier to separate foreground pixels from the background using the Otsu’s method to find the best threshold value. Following image binarization, we used blob algorithm to filter noise pixels. Then we developed grids by vertical and horizontal histogram projections to determine the possible area of every spot in an image. The binary image was further processed with the Sobel operator to obtain the edge image that was further processed with the circular Hough transform to determine the position and boundary of each spot. The determination of circular spots was based on the number of pixels in the accumulating cells in the parameter space. To reduce computation complexity, it is important to avoid identification errors by decreasing redundant accumulations. Using the gradient angle information in an edge image, we were able to use only half circle, +90 and -90 degrees between gradient angles, in the circular Hough transform. With this approach, we can not only reduce the influences of noises but also improve the accuracy of spot identification. The computation time was also reduced in half. After identifying the spots, the intensities of foreground and background pixels were used in the subsequent statistical analysis of microarray data. Comparing the performance of SPOTCapture software developed in this research with the commercialized software GenePix Pro 6.0, SPOTCapture has the precision rate of 98.6% and the recall rate of 98.3% while the precision rate and recall rate of GenePix Pro 6.0 was 97.5% and 97.9, respectively. The performance difference between these two approaches was statistically significant as the results were tested with the chi-square test. As for the determination of position and area of spots in a microarray image, SPOTCapture has a higher accuracy than the GenePix Pro 6.0 by 2.4%. Our method is also sensitive in resolving the problems of donut spots frequently occurred in microarray images. In addition, the parameter settings of our method are less complicated and require less manual intervention. Thus, with all these advantages, the SPOTCapture can be used as an efficient platform for the analyses of microarray images.