Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression

Multimodal medical imaging, which involves the simultaneous acquisition of different modalities, enhances diagnostic accuracy and provides comprehensive visualization of anatomy and physiology. However, this significantly increases data size, posing storage and transmission challenges. Standard imag...

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Veröffentlicht in:IEEE open journal of signal processing Jg. 6; S. 489 - 497
Hauptverfasser: Nicolau, Daniel S., Thomaz, Lucas A., Tavora, Luis M. N., Faria, Sergio M. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Artificial neural networks
Codec
Coding
Complexity
Computed tomography
Deep learning
Encoding
Generative adversarial networks
Generative predictive coding
Image coding
learning-based prediction
lossless image coding
Machine learning
Magnetic resonance imaging
Medical diagnostic imaging
Medical imaging
Medical services
multimodal medical imaging
Positron emission
Translation
versatile video coding
ISSN:2644-1322, 2644-1322
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Zusammenfassung:Multimodal medical imaging, which involves the simultaneous acquisition of different modalities, enhances diagnostic accuracy and provides comprehensive visualization of anatomy and physiology. However, this significantly increases data size, posing storage and transmission challenges. Standard image codecs fail to properly exploit cross-modality redundancies, limiting coding efficiency. In this paper, a novel approach is proposed to enhance the compression gain and to reduce the computational complexity of a lossless cross-modality coding scheme for multimodal image pairs. The scheme uses a deep learning-based approach with Image-to-Image translation based on a Generative Adversarial Network architecture to generate an estimated image of one modality from its cross-modal pair. Two different approaches for inter-modal prediction are considered: one using the original and the estimated images for the inter-prediction scheme and another considering a weighted sum of both images. Subsequently, a decider based on a Convolutional Neural Network is employed to estimate the best coding approach to be selected among the two alternatives, before the coding step. A novel loss function that considers the decision accuracy and the compression gain of the chosen prediction approach is applied to improve the decision-making task. The experimental results on PET-CT and PET-MRI datasets demonstrate that the proposed approach improves by 11.76% and 4.61% the compression efficiency when compared with the single modality intra-coding of the Versatile Video Coding. Additionally, this approach allows to reduce the computational complexity by almost half in comparison to selecting the most compression-efficient after testing both schemes.
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ISSN:2644-1322
2644-1322
DOI:10.1109/OJSP.2025.3564830