Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

Autofocus systems are essential in optical microscopy. These systems typically sweep the sample through the focal range and apply an algorithm to determine the contrast value of each image, where the highest value indicates the optimal focus position. As the optimal algorithm may vary according to the images' content, we evaluate the 15 most used algorithms in the field using 150 stacks of images from four different kinds of tissue. We use four measuring criteria and two types of analysis and propose a general methodology to apply to select the best fitting algorithm for any given application. In this paper, we present the results of this evaluation and a detailed discussion of different features: the threshold used for the algorithms, the criteria parameters, the analysis used, the bit depth of the images, their magnification, and the type of tissue, reaching the conclusion that some of these parameters are more relevant to the study than others, and the implementation of the proposed methodology can lead to a fast and reliable autofocus system capable of performing an analysis and selection of algorithms with no supervision required.