The growing adoption of biomedical machine vision algorithms to perform detection, segmentation, and classification tasks, is driving a shift in compression paradigms, progressively replacing perceptual quality by per...
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ISBN:
(纸本)9789082797091
The growing adoption of biomedical machine vision algorithms to perform detection, segmentation, and classification tasks, is driving a shift in compression paradigms, progressively replacing perceptual quality by performance of machine vision tasks as the target encoding optimization. Therefore, improving task performance rather than image quality has become a new research problem in biomedicalimage compression. This paper presents a contribution to extend the useful compression range from lossless to lossy while keeping the performance of biomedical machine vision algorithms. Automatic detection of mitochondrias in electron microscopy images, using a learning-based network (YOLO), is the case-study investigated in this work. Two types of new results are presented in regard to detection performance. In the first one, it is shown that compression ratios up to 15 can be used, for a maximum of 3% of detection loss. Then in the second one, by using compressed images for training, it is shown that the compression range can be increased up to 135 times, while missing less than 5% of the detections.
We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved i...
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ISBN:
(纸本)9781424404810
We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved in this popular biomedical application necessitate fast and efficient compression methods. We propose to use 3D prediction, temporal decorrelation and entropy coding with context modeling for encoding the fMRI scans after preprocessing with the region-of-interest (ROI) masking. The proposed algorithm is conceptually simple and can achieve fast implementation and efficient coding performance. We illustrate computer simulations to show advantages over conventional coding methods.
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