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|Title:||Volume processing of fluorescent laser confocal microscopic cellular images||Authors:||Chandrasekaran Indhumathi.||Keywords:||DRNTU::Engineering::Bioengineering||Issue Date:||2011||Source:||Chandrasekaran, I. (2011). Volume processing of fluorescent laser confocal microscopic cellular images. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||The advent of light microscope techniques, together with advances in fluorescent labeling technologies, has revolutionized the study of cellular processes. It has become a pivotal tool that has greatly impacted biological research at cellular and sub-cellular levels with advantages over conventional optical microscopy. However, several unique characteristics of confocal datasets pose serious challenges such as the noise and artifacts developed with confocal data and the intensity attenuation problem due to low axial resolution and light decay with depth. Currently, image processing, modeling, visualization and analysis on confocal microscopic datasets are, however, still done in 2D. In the case of traditional 2D slice-by-slice image processing strategy for the volumetric dataset, the image slices are analyzed only in the 2D plane where as the cross slice details (z axis) got ignored. On the other hand volumetric image processing usually takes into consideration the inter-slice information and also preserves image structures across slices. Hence, instead of using 2D slice-by-slice strategy, treating confocal scanned volumes as a whole has its advantages. In this report we provide a detailed literature study on various image-processing algorithms for confocal cellular image dataset. Our main goal of this research is to develop novel algorithms for 3D image processing methods for confocal cellular image enhancement, object feature extraction and subsequent volume visualization in an interactive and immersive VR environment.||URI:||http://hdl.handle.net/10356/44598||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Theses|
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