Ali Noureddine Ait, El Abbassi Ahmed, Bouattane Omar
Labo ERTTI, FST Errachidia, Moulay Ismail University of Meknes, Meknes, Morocco.
SSDIA Laboratory, ENSET-Mohammedia Hassan II University Casablanca, Casablanca, Morocco.
Multimed Tools Appl. 2023;82(5):6787-6805. doi: 10.1007/s11042-022-13635-z. Epub 2022 Aug 10.
Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm's execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations' extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version's execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.
通过分割技术进行图像处理是医学成像（如磁共振成像）中的一个重要阶段。其目的是分析人体中的不同组织。在研究领域，模糊集是保证稳健分类的最成功技术之一。空间模糊C均值算法（SFCM）是模糊C均值算法的变体之一，它考虑空间信息来处理噪声图像。为了减少这种迭代算法的执行时间，一种名为图形处理单元（GPU）的硬件SIMD架构被采用。在这项工作中，为在GPU上诊断、对比和实现三种不同的并行实现做出了巨大贡献。本文提出了一种对使用3×3窗口的SFCM进行并行实现的深入研究，即PSFCM，实验表明该算法虽复杂度高，但运行时间显著减少。实验结果表明，并行版本的执行时间比图像分割的顺序实现快约9.46倍。这种加速是在英伟达GeForce GT 740 m GPU上实现的。
