Least Information Loss (LIL) Conversion of Digital Images and Lessons Learned for Scientific Image Inspection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F16%3A43890404" target="_blank" >RIV/60076658:12520/16:43890404 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-31744-1_47" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-31744-1_47</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-31744-1_47" target="_blank" >10.1007/978-3-319-31744-1_47</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Least Information Loss (LIL) Conversion of Digital Images and Lessons Learned for Scientific Image Inspection

Popis výsledku v původním jazyce

Nowadays, most digital images are captured and stored at 16 or 12 bit per pixel integers, however, most personal computers can only display images in 8 bit per pixel integers. Besides, each microarray experiment produces hundreds of images which need larger storage space if images are stored in 16 or 12 bit. This is in most cases done by conversion of single images by an algorithm, which is not apparent to the user. A simple method to avoid the problem is converting 16 or 12-bit images to 8 bit by direct division of the 12-bit intervals into 256 sections and counting the number of points in each of them. Although this approach preserves the proportion of camera signals, it leads to severe loss of information due to losses in intensity depth resolution. The main aim of this article is introducing least information loss (LIL) algorithm as a novel approach to minimize the information loss caused by the transformation the primary camera signals (16 or 12 bit per pixels) to 8 bit per pixel. Least information loss algorithm is based on the omission of unoccupied intensities and transforming remaining points to 8 bit. This approach not only preserve information by storing intervals in the image EXIF file for further analysis, but also it improves object contrast for better visual inspection and object oriented classification. LIL algorithm may be applied also in image series where it enables comparison of primary camera data at scales identical over the whole series. This is particularly important in cases that the coloration is only apparent and reflect various physical processes such as in microscopy imaging. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

Least Information Loss (LIL) Conversion of Digital Images and Lessons Learned for Scientific Image Inspection

Popis výsledku anglicky

Nowadays, most digital images are captured and stored at 16 or 12 bit per pixel integers, however, most personal computers can only display images in 8 bit per pixel integers. Besides, each microarray experiment produces hundreds of images which need larger storage space if images are stored in 16 or 12 bit. This is in most cases done by conversion of single images by an algorithm, which is not apparent to the user. A simple method to avoid the problem is converting 16 or 12-bit images to 8 bit by direct division of the 12-bit intervals into 256 sections and counting the number of points in each of them. Although this approach preserves the proportion of camera signals, it leads to severe loss of information due to losses in intensity depth resolution. The main aim of this article is introducing least information loss (LIL) algorithm as a novel approach to minimize the information loss caused by the transformation the primary camera signals (16 or 12 bit per pixels) to 8 bit per pixel. Least information loss algorithm is based on the omission of unoccupied intensities and transforming remaining points to 8 bit. This approach not only preserve information by storing intervals in the image EXIF file for further analysis, but also it improves object contrast for better visual inspection and object oriented classification. LIL algorithm may be applied also in image series where it enables comparison of primary camera data at scales identical over the whole series. This is particularly important in cases that the coloration is only apparent and reflect various physical processes such as in microscopy imaging. (C) Springer International Publishing Switzerland 2016.

Druh

D - Stať ve sborníku

CEP obor

EI - Biotechnologie a bionika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-319-31743-4

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

10

Strana od-do

527-536

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Granada; Spain

Datum konání akce

20. 4. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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