MedGraph: a graph-based representation and computation to handle large sets of images

In order to process and analyze very large volumes of images, efficient representation and structuring techniques are required. Since, current computing machines can provide large memory size, trading off reasonable amount of memory in order to achieve efficient and parallelizable representation of images is preferable. In this paper, we propose a new structure to represent and store images based on in-memory graph concept. Our method of computation provides a faster execution time than the traditional array-based representation. Each pixel of an image is represented as a one node in the graph. In addition, nodes have pointers to other neighboring nodes (pixels). The structure represents an image as a grid of connected linked lists and each grid is connected to other grids. Using our method, an image can be represented in one of three different representations which are: octal linked list, quadratic linked list, and dual linked list representations. We provide experiments and evaluations using the dual linked list representation as it requires less memory space. We apply our methodology for medical images as a proof of the concept to find a region of interest in an image. We have collected and used real medical images to build and process the graph which we call MedGraph. Our experimental results show that the proposed MedGraph technique improves the searching time for finding a region of interest when compared to the traditional representation. It is worth mentioning here that MedGraph is a generic representation strategy that can be applied to any type of images, although this paper uses medical images as a proof of the concept.