A Dataflow Implementation of Region Growing Method for Cracks Segmentation

Abstract

Region growing is an image segmentation algorithm extremely useful for continuous regions extraction. It defines an initial set of seeds, according to a specific criteria, and iteratively aggregates similar neighbor pixels. The algorithm converges when no pixel aggregation is performed in a certain iteration. Within this research project, region growing is employed for the segmentation of cracks in images of ore particles acquired by scanning electron microscopy (SEM). The goal is to help scientists evaluate the efficiency of cracking methods that would improve metal exposure for extraction through heap leaching and bioleaching. However, this is a computational intensive application that could take hours to analyze even a small set of images, if executed sequentially. This paper presents and evaluates a dataflow parallel version of the region growing method for cracks segmentation. The solution employs the Sucuri dataflow library for Python to orchestrate the execution in a computer cluster. Since the application processes images of different sizes and complexity, Sucuri played an important role in balancing load between machines in a transparent way. Experimental results show speedups of up to 26.85 in a small cluster with 40 processing cores and 23.75 in a 36-cores machine.