LOSSY COMPRESSION OF BIOMEDICAL IMAGES FOR COMPUTER VISION ANALYSIS

• Main Author: Paulo, Edgar da Silva
• Publication Date: 2024
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.8/10029
• topic: Biomedical Images; Electron Microscopy Images; Lossy and near Lossless Compression; YOLO; Detectron2; HEVC; Region Coding; Medical Image Compression

The exponential increase in medical and biomedical data acquisition is compelled by technological advances, namely in the imaging field. However, this exponential growth brings with it challenges in terms of processing capacity, transmission, and data storage. In response to this growing demand, increasingly efficient solutions have emerged, especially through computer vision for automatic image analysis and compression algorithms. This dissertation aims, on the one hand, to evaluate the performance of computer vision systems on previously compressed biomedical images. On the other hand, it increases the useful range of image variations, almost lossless and lossy, decreasing the impact of the change added by this method on the performance of computer vision algorithms in biomedical image analysis. In this sense, YOLO and Detectron2 are employed to evaluate the impact of coding distortion on their ability to detect mitochondria in electron microscopy images. The results of this study reveal that although the distortion introduced by compression affects their detection performance, it is negligible at lower compression ratios. Furthermore, two proposals are presented to improve the useful compression ratio, keeping the images characteristics that allow to perform the automatic detection of mitochondria. On the one hand, it is demonstrated that the proposed training methodology, which incorporates compressed versions of the original data during training, mitigates the impact of distortion on the performance of computer vision algorithms; on the other hand, allocating higher quality levels to regions of interest, compared to background elements, helps to sustain high performance at compression rates where computer vision algorithms typically start to lose effectiveness. These approaches allow the extension of the compression range with little impact on detection performance, thus contributing to the improvement of data processing, storage, and transmission in biomedical applications.