DocumentCode :
2518728
Title :
SEGMENTATION OF NUCLEI IN CONFOCAL IMAGE STACKS USING PERFORMANCE BASED THRESHOLDING
Author :
Beaver, William ; Kosman, David ; Tedeschi, Gary ; Bier, Ethan ; McGinnis, William ; Freund, Yoav
Author_Institution :
Dept. of Comput. Sci. & Eng., UCSD, La Jolla, CA
fYear :
2007
fDate :
12-15 April 2007
Firstpage :
53
Lastpage :
56
Abstract :
Combinatorial transcriptional fluorescent in situ hybridization (CT-FLSH) is a confocal fluorescence imaging technique enabling the detection of multiple active transcription units in individual interphase diploid nuclei. As improved combinatorial labeling methods allow simultaneous measurement of gene activities to expand from five genes in a single embryo or tissue section to upward of twenty genes, transforming image stacks into usable data becomes an increasingly labor in tensive task, in this paper we describe our progress towards a method for the computational analysis of confocal images from Drosophila melanogastar that involves the segmentation of the cell nuclei and of nascent transcription sites of specific genes. Using image processing and machine learning algorithms, we allow experimentalists to reiteratively tune and improve the analysis system to reflect biological reality
Keywords :
biological tissues; biomedical measurement; biomedical optical imaging; cellular biophysics; computer vision; fluorescence; genetics; image segmentation; learning (artificial intelligence); medical image processing; Drosophila melanogastar; cell nuclei; combinatorial labeling methods; combinatorial transcriptional fluorescent in situ hybridization; confocal fluorescence imaging; confocal image stacks; confocal images; embryo; gene activities; image processing; interphase diploid nuclei; machine learning algorithms; multiple active transcription units; nascent transcription sites; nuclei segmentation; performance based thresholding; reiterative tuning; tissue section; transcription unit detection; Algorithm design and analysis; Biology computing; Embryo; Fluorescence; Image analysis; Image processing; Image segmentation; Labeling; Machine learning algorithms; Nuclear measurements;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0672-2
Electronic_ISBN :
1-4244-0672-2
Type :
conf
DOI :
10.1109/ISBI.2007.356786
Filename :
4193220
Link To Document :
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