By Oleg S. Pianykh
Making an exceptional diagnostic photo is just the start; holding it solid and diagnostically sound is a way more tough proposition, one who is usually ignored or forgotten via medical practitioners. With something electronic, the idea of chronic unique caliber opens a Pandora’s field of scientific fiascos. Poorly chosen photo interpolation, thoughtlessly used compression, burdened photograph enhancement thoughts and so on can rework an outstanding unique right into a dead muddle of pixels. This ebook is devoted to studying higher techniques. meant for physicians, scientific practitioners and purposes experts, it offers a well-rounded creation to significant diagnostic picture housework. The ebook provides crucial elements of secure electronic snapshot workflows, ranging from the elemental functional implications and steadily uncovering the underlying ideas and algorithms. With an easy-to-follow, down-to-earth presentation sort, the textual content permits you to optimize your diagnostic imaging initiatives and fix the dots of clinical informatics.
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Additional resources for Digital Image Quality in Medicine
8 6 Apart from compression, the authors also used several image format conversion steps (DICOM, BMP, AVI) which could have made image quality even worse. 7 I can tell from my own experience that older radiologists seem to be more tolerant to image artifacts, and some publications confirm this point (Erickson et al. 2010). 8 Don’t ask your software vendor – why would they care? 38 4 Image Compression ᭤ Nota bene: Keep in mind that there is absolutely no straightforward way to associate compression ratio Rc with compressed image quality; the latter depends on many objective and subjective factors such as image texturing or human vision phenomena, generally ignored by the compression algorithms.
10). Can we do better than Keys interpolation? We certainly can – the road to perfection is infinite. Using more polynomial “pieces” hi(x) in Eq. 9), and higher degrees of these polynomials will enable one to accurately interpolate more than cubic functions (Keys developed a fourth-order interpolation as well). Moreover, instead of high-order polynomial accuracy, one can use other different criteria of interpolation kernel optimality: highest order of approximation per given kernel “support size” N, minimal highest interpolation error (minmax problem), best preservation of the image frequency spectrum (image details), and so on.
Clinical data volumes – medical imaging in particular, as the most megabyte-hungry – went through the hospital roofs, turning data storage and transmission into the most expensive enterprise of the digital age. Consequently, data compression has become an integral part of modern medical workflow, even if you never thought about it before. In fact, you can get yourself into serious trouble if you keep ignoring compression, as most of us did and still prefer to do. So let’s spend some time studying how it works, and how it changes your diagnostic images.