Algorithms for leukaemia image edge detection technique - Ali Mohammed Alshorman, Moath (Institute of Engineering Mathematics, Universiti Malaysia Perlis, 2017)
The main objectives of this study are to detect the edges of leukaemia through filtering, sorting process, kernel multiphase convolution, binarization and transformation techniques; to employ Sobel, Prewitt and Robert edge detection to recognize the pattern of the detected leukaemia cells in the white blood image; to utilize Ant Colony Optimization (ACO) algorithm to detect, extract and recognize the edge boundaries of the leukaemia cells from the white blood cell image; to compare the performance of the gradient edge detection methods (Sobel, Prewitt and Robert) with the ACO method to determine the optimal edges of the leukaemia cells; to describe the features of white and red blood cells based the detected edges through the visual comparison.