Pregled bibliografske jedinice broj: 647987
Application of Gaussian Mixture Models with Expectation Maximization in Bacterial Colonies Image Segmentation for Automated Counting and Identification
Application of Gaussian Mixture Models with Expectation Maximization in Bacterial Colonies Image Segmentation for Automated Counting and Identification // IFMBE Proceedings Volume 41 / Roa Romero, Laura M. (ur.).
Cham : Heidelberg : New York : Dordrecht : London: Springer, 2014. str. 388-391 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 647987 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Application of Gaussian Mixture Models with Expectation Maximization in Bacterial Colonies Image Segmentation for Automated Counting and Identification
Autori
Silva Maretić, Igor ; Lacković, Igor
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
IFMBE Proceedings Volume 41
/ Roa Romero, Laura M. - Cham : Heidelberg : New York : Dordrecht : London : Springer, 2014, 388-391
ISBN
978-3-319-00845-5
Skup
XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
Mjesto i datum
Sevilla, Španjolska, 25.09.2013. - 28.09.2013
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Image segmentation; Object recognition; Colony counting; Gaussian Mixture Model; Expectation-maximization algorithm
Sažetak
This paper presents an approach to identification of homogenous bacterial colonies grown on agar in Petri dishes. The aim of the work was to recognize regions in digital images of Petri dishes were homogenous bacterial colonies were developed as well as to estimate their size. Isolation of bacterial cultures’ region from the dish was achieved by image segmentation based on histogram analysis. The histogram was parameterized using Gaussian Mixture Model with Expecta-tion Maximization. This algorithm gave a good estimation of the actual gray level distribution and was able to separate merging distribution of two different objects. However, it performed poorly with the presence of outliers. The algorithm performance was also dependent on initial model and the number of Gaussians chosen. Overall, for images taken under controlled conditions the application of Gaussian Mixture model with Expectation Maximization proved to be successful and efficient approach to image segmentation of bacterial colonies. Final step was separation of circular-like colonies from non-circular ones. This was achieved using appropriate shape identification techniques. Validation of the proposed segmentation process was demonstrated using images from the microbiology laboratory, some artificially generated images and images downloaded from the Internet.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika
POVEZANOST RADA
Projekti:
036-0362979-1554 - Neinvazivna mjerenja i postupci u biomedicini (Tonković, Stanko, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Igor Lacković
(autor)
