An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys

Digital image analysis is a valuable tool for the quantification of microstructural morphology, such as the size and clustering distributions of second-phase particles. However, the first step to such analysis is the segmentation of digital micrographs to identify the features of interest. Since the outcome of this step will have a significant influence on all subsequent processing, accurate segmentation is of the utmost importance. Grayscale images of the microstructures of three automotive aluminum alloys are used (AA5182, AA5754, and AA6111) to compare the abilities of several automatic global image thresholding techniques. The minimum cross-entropy method of Li and Lee [Pattern Recognit. 30 (1993) 617] was found to work well for all alloys when combined with the cost function of Yen et al. [IEEE T. Image Process. 4 (1995) 370].