Abstract
This special issue on Real-Time Processing of Medical Images addresses the current state-of-the-art in the field of parallel programming for medical imaging applications and the future trends in real-time medical image processing, including parallel computing (computational models, parallel algorithms and hierarchical memory optimizations), real-time implementation of embedded medical image processing applications on dedicated architectures and/or parallel architectures. We are very grateful to the reviewers who provided valuable comments and suggestions to improve the quality of the papers accepted for this special issue. Brief outlines of these papers are stated below: The first paper by Yee Hui Lee, Mohamed Khalil-Hani, Rabia Bakhteri and Vishnu P. Nambiar presents a real-time image acquisition system with an improved image quality assessment module to acquire high quality Near Infrared Images. The NIR image quality assessment module utilizes improved two-dimensional entropy and mask-based edge detection algorithms. In this work, the effectiveness of the proposed NIR image acquisition system is demonstrated through the implementation of a complete finger-vein biometric authentication system. The proposed module is implemented as an embedded system on a Field Programmable Gate Array (FPGA). The proposed system is able to achieve a recognition accuracy of 0.87% equal error rate (EER) and can handle real-time processing at 15 frames/second. The second paper by Faten Chaieb, Tarek Ben Said, Sabra Mabrouk and Faouzi Ghorbel presents a segmentation method from four-phase computed tomography images. The proposed method is based on the combination of the Expectation – Maximization algorithm and the Hidden Markov Random Fields. The segmentation method is applied on a Volume Of Interest (VOI) that decreases the number of processed voxels. In order to accelerate the classification steps within the segmentation process, a bootstrap resampling scheme is applied. The proposed method is speeded up by adopting a bootstrap sampling approach on a semi-automatic selected VOI.
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