Artificial Systems Can Complement Human Vision in Medical Imaging
Keywords:
texture analysis, computer-assisted radiology, media cybernetics, radiomics, hugues gentillon, mazda, artificial intelligence, differential diagnosis, computational visual cognition.Abstract
Texture analysis is an emerging field; and it is just beginning to integrate with radiology. Carrying out research with thousands of images can be overwhelming, without an effective and efficient sorting algorithm. The aim of this experiment was to develop a sample selection-elimination protocol for a large research project seeking to compare fetal 1.5- tesla versus 3-tesla magnetic resonance images. Firstly, we had to find optimal methods for image selection. In a compiled database of 1.5-tesla and 3-tesla images, we began by manually selecting sequences based on discernible-anatomical structures (ventricle, thalamus, grey matter, white matter). Then 1.5-tesla and 3-tesla image batches were categorized into two groups based on gestational age (i.e. first group: 20-28 week; second group: 29 week). The final stage was sample elimination by variance and by real bit-depth
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