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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

EI - Biotechnology and bionics

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2016

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-319-31743-4

ISSN

0302-9743

e-ISSN

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Number of pages

10

Pages from-to

527-536

Publisher name

Springer

Place of publication

Berlin

Event location

Granada; Spain

Event date

Apr 20, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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