Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.

Fast reconstructed radiographs from octree-compressed volumetric data.

PURPOSE: Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively slow using the conventional ray-casting algorithm. Image-guided radiation therapy systems using DTS to verify target location require a large number DRRs to be precomputed since there is insufficient time within the automatic image registration procedure to generate DRRs and search for an optimal pose. METHOD: DRRs were rendered from octree-compressed CT data. Previous work showed that octree-compressed volumes rendered by conventional ray casting deliver a registration with acceptable clinical accuracy, but efficiently rendering the irregular grid of an octree data structure is a challenge for conventional ray casting. We address this by using vertex and fragment shaders of modern graphics processing units (GPUs) to directly project internal spaces of the octree, represented by textured particle sprites, onto the view plane. The texture is procedurally generated and depends on the CT pose. RESULTS: The performance of this new algorithm was found to be 4 times faster than that of a ray-casting algorithm implemented using NVIDIA™Compute Unified Device Architecture (CUDA™) on an equivalent GPU (~95 % octree compression). Rendering artifacts are apparent (consistent with other splatting algorithm), but image quality tends to improve with compression and fewer particles are needed. A peak signal-to-noise ratio analysis confirmed that the images rendered from compressed volumes were of marginally better quality to those rendered using Gaussian footprints. CONCLUSIONS: Using octree-encoded DRRs within a 2D/3D registration framework indicated the approach may be useful in accelerating automatic image registration.

GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.

Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC architecture.