Childhood leukaemia demands meticulous blood cell analysis for diagnosis, focusing on morphological irregularities like asymmetry and abnormal cell counts. Traditional manual diagnosis via microscopic blood smear images suffers from reduced reliability, time intensiveness, and observer variability. Computer-aided diagnostic (CAD) systems address these challenges. Integrating real-time image pre-processing and segmentation ensures swift operation, reducing the CAD system processing time. This enhances its overall effectiveness, enabling timely medical intervention and better patient outcomes. This study aims to simplify the algorithmic complexity of pre-processing steps, including bilateral filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE), alongside the segmentation stage involving morphological operations and the watershed algorithm. This work proposes a parallel implementation utilizing OpenMP and CUDA, evaluating its performance using accuracy and Intersection over Union (IoU) metrics along with computing time and algorithmic complexity. It highlights the benefits of parallel processing in enhancing efficiency and and accuracy in blood cell analysis.
Video super-resolution reconstruction consists of generating high-resolution frames by processing low-resolution ones. This process enhances the video quality, allowing the visualisation of fine details. Moreover, it can be considered a primary step in a video processing pipeline for further applications, such as object detection, classification and tracking from uncrewed aerial vehicles (UAV). For this reason, the super-resolution process should be performed quickly and accurately. Implementing a real-time video super-resolution method through parallel programming contributes to the efficiency of this pipeline. This work proposes two parallel super-resolution approaches for videos taken from UAVs: one using multi-core CPUs and another on a GPU architecture. The method is based on sparse representation and Wavelet transforms. First, it makes an edge correction performed in the Wavelet domain, then employs dictionaries previously trained with k-Singular Value Decomposition (k- SVD) to reconstruct the Wavelet subbands of the frames, and the high-resolution frames are computed from the Inverse Discrete Wavelet Transform (IDWT). The performance of this method was measured with the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Edge Preservation Index (EPI). The implementations are tested in a workstation with a Ryzen multi-core processor and a CUDA-enabled GPU; furthermore, they are compared with the non-parallel method regarding algorithm complexity and computing time.
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