New software tool Pixasonics turns biomedical images into sound
Modern biomedicine increasingly relies on enormous amounts of image data. Although computers can do part of the analysis work, biologists often still need to look through the images themselves. This takes time.
New software tool Pixasonics transforms biomedical images into sound, offering a novel way to analyze complex data. Biomedical research increasingly relies on vast image datasets, but visually inspecting each image can be time-consuming. Dr. Bálint Laczkó, in collaboration with the University of Oslo, developed Pixasonics to turn image data into sound, a process known as image sonification. This innovative approach could help detect patterns that are challenging to spot with the naked eye.
Pixasonics, written in Python and openly available on GitHub, is a toolbox of ready-made building blocks for researchers working with image data. By plugging these blocks into existing programs, researchers can integrate image sonification into their workflows seamlessly. For instance, in the Autorhythm project, researchers are studying the autophagy process in cells, which involves analyzing thousands of detailed time-lapse images.
With Pixasonics, researchers can upload these images and assign specific tasks to the tool. Pixels in the image are translated into sound, with bright pixels producing high, beeping tones and dark pixels generating deeper, rumbling tones.
As researchers play the audio representation alongside the image, they can hear how the subject changes over time. This auditory approach can reveal patterns and changes that might be missed during visual inspection. The combination of vision and hearing—each with its strengths—can make large image datasets more manageable and insightful. Laczkó hopes to further develop Pixasonics, hold workshops, and conduct user studies to explore its full potential in biomedical research.
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