[Paper Review] A Survey of Multithreading Image Analysis

Elham Sagheb Hossein Pour|arXiv (Cornell University)|Jun 15, 2015

Digital Media Forensic Detection23 references3 citations

TL;DR

This survey reviews the integration of multithreading techniques into digital image analysis to enhance computational performance and enable real-time processing. By analyzing recent implementations using OpenMP, Pthreads, and Java multithreading, the paper demonstrates significant speedups—up to 50% reduction in computation time—while highlighting challenges in concurrency management and tooling complexity.

ABSTRACT

Digital image analysis has made a big advance in many areas of enterprise applications including medicine, industry, and entertainment by assisting the extraction of semantic information from digital images. However, its large computational complexity has been a trouble to most real-time developments. While image analysis in general has been studied for a log period in computer science community, the use of multithreading strategy as the most efficient improving computational capacity technique has been limited so far. In this survey an attempt is made to explain the current knowledge and so far progresses in incorporating image analysis with multithreading approaches. The present work also provides insights and tendencies for the possible future enhancement of multithreading image analysis.

Motivation & Objective

• To examine the current state of multithreading integration in digital image analysis for improved computational efficiency.
• To identify performance gains and implementation challenges in multithreaded image processing across different programming models and hardware platforms.
• To evaluate the scalability and practicality of multithreading in real-time and large-scale image analysis workloads.
• To explore future research directions, including Big Data infrastructures and SaaS-based frameworks for medical and high-resolution image processing.

Proposed method

• Systematic review of peer-reviewed literature from 1998 to 2015 on multithreaded image analysis techniques.
• Analysis of multithreading implementations using OpenMP, Pthreads, and Java concurrency models in image processing pipelines.
• Evaluation of performance improvements in image convolution, filtering, segmentation, and registration tasks across single-core and multi-core CPUs.
• Comparison of programmability and performance across operating systems (Windows vs. Linux) and threading libraries.
• Case studies of real-world frameworks such as FAST (FrAmework for heterogeneouS medical image compuTing and visualization) using multithreading.
• Synthesis of trends, limitations, and future research directions based on surveyed works and implementation patterns.

Experimental results

Research questions

• RQ1 How do multithreading techniques improve the performance of digital image analysis algorithms such as convolution and filtering?
• RQ2 What are the key challenges in implementing multithreaded image processing in terms of debugging, resource management, and portability?
• RQ3 How do different threading models (e.g., OpenMP, Pthreads, Java) compare in performance and ease of implementation for image processing tasks?
• RQ4 To what extent can multithreading enable real-time and scalable image analysis in medical and high-resolution imaging applications?
• RQ5 What future architectural enhancements (e.g., Big Data, SaaS) can further improve multithreaded image analysis systems?

Key findings

• Multithreading reduced computational time by nearly 50% in PDE-based image registration using OpenMP on dual-core systems.
• Image convolution performance improved significantly on both Windows and Linux platforms using Pthreads and compiler directives, with better programmability in Linux.
• Java-based multithreading achieved performance gains on both single-core and multi-core CPUs, with optimal results for small image sizes and complex algorithms on multi-core systems.
• The FAST framework demonstrated superior performance and usability over ITK and VTK in medical image processing by leveraging multithreading.
• Despite performance gains, implementation complexity, debugging difficulties, and concurrency management remain major barriers to widespread adoption.
• Future integration with Big Data platforms and SaaS architectures is identified as a promising path to enhance scalability and reliability for large-scale image analysis.