Digital Image Quality in Medicine by Oleg S. Pianykh

By Oleg S. Pianykh

Making an outstanding diagnostic photograph is barely the start; maintaining it sturdy and diagnostically sound is a way more tricky proposition, person who is frequently overlooked or forgotten through medical practitioners. With something electronic, the idea of continual unique caliber opens a Pandora’s field of scientific fiascos. Poorly chosen picture interpolation, thoughtlessly used compression, careworn snapshot enhancement recommendations etc can remodel an outstanding unique right into a lifeless litter of pixels. This booklet is devoted to studying greater concepts. meant for physicians, scientific practitioners and functions experts, it offers a well-rounded creation to significant diagnostic photo home tasks. The ebook provides crucial points of secure electronic photo workflows, ranging from the fundamental useful implications and steadily uncovering the underlying suggestions and algorithms. With an easy-to-follow, down-to-earth presentation kind, the textual content enables you to optimize your diagnostic imaging initiatives and fix the dots of scientific informatics.​

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Sure, you can’t really call lossy compression a noise-removal tool; you need a set of totally different algorithms to do it right. But it is not unfeasible to design a noise-removal algorithm that will improve diagnostic image quality and reduce compressed image size at the same time. 2 σ caused by noise 1 13 20 26 JPEG compression ratio Rc Fig. 6 Variance σ observed in 3 mm vessel diameter on phantom coronary angiography images, based on Fritsch and Brennecke (2011) results. For relatively low compression ratio Rc, the original X-ray noise dominates the lossy compression artifacts.

For that reason, noise has always been the principal enemy of data compression: you cannot achieve high Rc with noisy data. On the other hand, noise is the principal enemy of diagnostic image interpretation as well: it occludes important details; it simply pollutes the image (Fig. 5). This points us to a couple of interesting ideas: • What we lose by applying lossy compression may not be so important. Sometimes it may be just pure, useless noise. When lossy compression threshold ε > 0 happens to be close to that of the natural pixel noise, the compression alterations will stay hidden in the noise, without touching anything diagnostic.

Rc = 20 substantial image blur. It happens for the same reasons: when JPEG2000 has to achieve high Rc, it starts sacrificing the fine local details, as taking the most memory to store. The strategy inevitably results in blurred, detail-less areas, very much like we saw with our good old friend, JPEG. Another theoretically useful functionality explored by JPEG2000 authors was the ability to encode and stream different image regions using different strengths of compression (Rc). It was expected that this would significantly reduce the volume of data being downloaded: if the user is only going to look at a specific area, only this area needs to be transmitted with the highest quality.

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