Optimizacija slikovnih operatorjev za segmentacijo mikroskopskih slik

Avberšek, Tomaž

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Title:	Optimizacija slikovnih operatorjev za segmentacijo mikroskopskih slik
Authors:	ID Avberšek, Tomaž (Author) ID Holobar, Aleš (Mentor) More about this mentor... ID Divjak, Matjaž (Comentor) ID Munda, Miha (Comentor)
Files:	MAG_Avbersek_Tomaz_2018.pdf (3,38 MB) MD5: B4216C848B1AD154E932C8DB664EC9BA PID: 20.500.12556/dkum/dc142d31-5bb2-4b06-a603-dec4d1435636 MAG_Avbersek_Tomaz_2018.zip (28,88 KB) MD5: 8D1540B95D0DF9DF5FCAB2477F610628 PID: 20.500.12556/dkum/0e24beda-37b6-40a0-8d2b-d1504bf98fef
Language:	Slovenian
Work type:	Master's thesis/paper
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
Place of publishing:	[Maribor
Publisher:	T. Avberšek
Year of publishing:	2018
PID:	20.500.12556/DKUM-70636
UDC:	004.451.354(043.2)
COBISS.SI-ID:	21718806
NUK URN:	URN:SI:UM:DK:HCVQT8XO
Publication date in DKUM:	24.08.2018
Views:	1936
Downloads:	146
Metadata:
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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