Quantitative Image Analysis for Radiological Image Understanding

Dear Colleagues,

Biomedical image analysis is able to characterize diseases multi-parametrically. It is therefore a key component in modern frameworks and platforms, enabling innovative concepts with regard to precision imaging procedures and thus supporting tasks in precision medicine. As a main approach of the new paradigm in radiology, "quantitative imaging" aims at extracting measurable features from medical images to characterize status, severity, or degree of change of a disease, injury, or chronic condition.

Thanks to the great success of computer vision and artificial intelligence areas in the last two decades, and supported by recent developments in high-performance computing and cloud computing infrastructures, it is now possible to massively and systematically explore the enormous amounts of images and associated metadata (i.e., multiparametric data) captured by radiologists and physicians in clinical routine to identify objectively measurable features. With this, it is possible to identify patient-specific image features that have so far been withheld from evaluation, paving the way to the emergent field of quantitative radiomics. Radiomics extracts features from medical images that quantify phenotypic characteristics in an automated, high-throughput manner. Radiomics features have been demonstrated to be useful in several medical image analysis scenarios, e.g., assessing malignancy in cancer research or detecting Alzheimer’s disease in neuroscience.

The aim of this Special Issue is to provide researchers with a forum to present their original and disruptive research in the field of quantitative imaging for improving the understanding of radiological images and help to radiologists/physicians in the early detection (screening and early diagnosis) of diseases.

Prof. Dr. Miguel Angel Guevara Lopez
Dr. Gerd Reis
Prof. Dr. Ignacio Blanquer Espert
Guest Editors

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