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Title:Optimizacija slikovnih operatorjev za segmentacijo mikroskopskih slik
Authors:Avberšek, Tomaž (Author)
Holobar, Aleš (Mentor) More about this mentor... New window
Divjak, Matjaž (Co-mentor)
Munda, Miha (Co-mentor)
Files:.pdf MAG_Avbersek_Tomaz_2018.pdf (3,38 MB)
MD5: B4216C848B1AD154E932C8DB664EC9BA
 
.zip MAG_Avbersek_Tomaz_2018.zip (28,88 KB)
MD5: 8D1540B95D0DF9DF5FCAB2477F610628
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Delo obravnava obdelavo mikroskopskih slik celic oz. tkiv na podlagi večstopenjske digitalne obdelave slik. Namen je bila optimizacija morfoloških operatorjev za segmentacijo mikroskopskih slik. V delu sta uporabljena Bayesova segmentacija in genetski algoritem pri iskanju dobrega kromosoma za optimiziranje rezultatov slikovne segmentacije. Pri učenju in testiranju smo uporabili slike modro in rjavo obarvanih celic z merilno skalo 50 μm. Učno množico je sestavljalo sedem, testno pa enajst slik velikosti 2088 x 1550 pikslov. Natančnost segmentacije je bila boljša pri rjavih pikslih in je v povprečju dosegla 76 odstotkov pri metriki pravilno pozitivno zaznanih pikslov. Z 51 odstotki pri isti metriki se je segmentacija modrega razreda odrezala precej slabše.
Keywords:genetski algoritem, segmentacija, celice, optimizacija, Bayes
Year of publishing:2018
Publisher:T. Avberšek
Source:[Maribor
UDC:004.451.354(043.2)
COBISS_ID:21718806 New window
NUK URN:URN:SI:UM:DK:HCVQT8XO
Views:720
Downloads:72
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:01.06.2018

Secondary language

Language:English
Title:Optimization of image operators for microscopic image segmentation
Abstract:The work focuses on microscopic image processing using multi-layered image processing technique for microscopic image segmentation. The purpose of the work was to optimize morphological operators for microscopic image segmentation. We have used Bayesian segmentation and a genetic algorithm for result optimisation. When learning and testing, we have used images of cells with brown and blue colouring agent with a measuring scale 50 μm. The learning set was composed of 7 images, whereas the testing set had 11 images of 2088 x 1550 pixels. The segmentation yielded better results for the brown cells with the True Positive metrics amounting to 76%. With 51% with the same metrics, the blue class did not perform as good as the brown class.
Keywords:genetic algorithm, segmentation, cells, optimisation, Bayes


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