Beschreibung
Automatic image segmentation is one of the central problems in the area of computer vision. It is commonly employed in many tasks such as locating objects in satellite images (roads, forests, etc.), traffic control systems, face recognition, etc. But, in particular it plays an important role in biomedical imaging, with various different applications, one of them dealing with Segmentation of Living Cells. In this thesis, we use three different segmentation techniques, namely, region growing segmentation technique, watershed segmentation and texture segmentation to accurately extract the shapes of membranes and nuclei from the inverted microscopy images, taken throughout the monolayer formation of BGM-70 (Baby Grevit Monkey) cell culture. This strategy is a prerequisite for an accurate quantitative analysis of cell shape and morphogenesis during organogenesis and is the basis for an integrated understanding of biological processes. The segmentation of cellular structures is achieved by first normalizing the microscopic images by performing CLAHE operation followed by de-noising by morphological opening and filling of the holes and then applying segmentation algorithm for cell shape r
Autorenporträt
Bazila Hashia has received her BE in Electronics and Communication Engineering from University of Kashmir,India and then received her MTech from NIT Srinagar in the year 2010. And has worked as lecturer in thedepartment of Electronics and Communication from Aug 2010 to Dec 2012. Presently she is a research scholar atNIT Srinagar in the departmen
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