Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to \(\sim 19.5\times \) and \(\sim 37\times \) speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research.

图像纹理提取和分析是计算机视觉的基本步骤。特别地，考虑到生物医学领域，定量成像方法越来越重要，因为它们传达了科学和临床相关的信息用于预测，预后和治疗反应评估。在这种情况下，放射学方法正在促进可能对临床实践产生重大影响的大规模研究。在这项工作中，我们提出了一种称为CHASM（Cuda，HAralick＆SoM）的新方法，该方法在图形处理单元（GPU）上得到了加速，用于基于Haralick特征和自组织图（SOM）进行定量成像分析。Haralick特征提取步骤依赖于灰度共生矩阵，这对于以高位深度为特征的医学图像在计算上是繁重的。下游分析利用SOM，目的是以无监督的方式识别像素的基础群集。CHASM旨在利用现代GPU的并行计算功能。通过分析卵巢癌计算机断层扫描图像，CHASM可达到与相应的C ++编码顺序版本相比，分别为Haralick特征提取和SOM执行提供了\（\ sim 19.5 \ times \）和\（\ sim 37 \ times \）加速因子。这样的计算结果指出了GPU在临床研究中的潜力。