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

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Abstract

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.

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Acknowledgments

The authors would like to thank the Deanship of Research at the Jordan University of Science and Technology for funding this work, grant number 20150050.

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Authors and Affiliations

  1. Department of Computer Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Moath Jarrah

  2. Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Muneera Al-Quraan, Yaser Jararweh & Mahmoud Al-Ayyoub

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  1. Moath Jarrah
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  2. Muneera Al-Quraan
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  3. Yaser Jararweh
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  4. Mahmoud Al-Ayyoub
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Correspondence to Moath Jarrah.

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Jarrah, M., Al-Quraan, M., Jararweh, Y. et al. MedGraph: a graph-based representation and computation to handle large sets of images. Multimed Tools Appl 76, 2769–2785 (2017). https://doi.org/10.1007/s11042-016-3262-0

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  • DOI: https://doi.org/10.1007/s11042-016-3262-0

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