Ms. Yulia Arzhaeva Profile

Yulia Arzhaeva

at CSIRO

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Proceedings Article | 13 March 2010 Paper

Mosaicing of microscope images in the presence of large areas with insufficient information content

Yulia Arzhaeva, Pascal Vallotton, Changming Sun

Proceedings Volume 7623, 762338 (2010) https://doi.org/10.1117/12.844141

KEYWORDS: Image registration, Microscopes, Microscopy, Sun, Visualization, Cameras, Bacteria, Image analysis, Luminescence, Image quality

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In virtual microscopy, multiple overlapping fields of view are acquired from a large slide using a motorized microscope stage that moves and focuses the slide automatically. A virtual slide is reconstructed by combining digitally saved fields of view into an image mosaic. A seamless reconstruction requires the correction of unknown positioning errors of the stage. This is usually done by automatically estimating alignment parameters of the tiles in the image mosaic. But finding accurate alignment parameters can be inhibited by the presence of tiles that lack information content in the areas of overlap. In this work we propose a new mosaicing method that accesses information content of each overlap and performs pairwise registrations of adjacent tiles only if the content of their overlap is deemed sufficient for successful registration. For global positioning of tiles a stitching path is found by tracing such content-rich overlaps. We tested the proposed algorithm on bright field and fluorescence microscope images and compared the results with those of an existing algorithm based on simultaneous estimation of global alignment parameters. It is shown that the new algorithm improves perceived image quality at boundaries between tiles. Our method is also computationally efficient since it performs no more than one pairwise registration per tile on average.

Proceedings Article | 15 March 2006 Paper

Improving computer-aided diagnosis of interstitial disease in chest radiographs by combining one-class and two-class classifiers

Yulia Arzhaeva, David Tax, Bram van Ginneken

Proceedings Volume 6144, 614458 (2006) https://doi.org/10.1117/12.652208

KEYWORDS: Lung, Image classification, Chest imaging, Data modeling, Computer aided diagnosis and therapy, Radiography, Statistical analysis, Standards development, Receivers, Classification systems

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In this paper we compare and combine two distinct pattern classification approaches to the automated detection of regions with interstitial abnormalities in frontal chest radiographs. Standard two-class classifiers and recently developed one-class classifiers are considered. The one-class problem is to find the best model of the normal class and reject all objects that don't fit the model of normality. This one-class methodology was developed to deal with poorly balanced classes, and it uses only objects from a well-sampled class for training. This may be an advantageous approach in medical applications, where normal examples are easier to obtain than abnormal cases. We used receiver operating characteristic (ROC) analysis to evaluate classification performance by the different methods as a function of the number of abnormal cases available for training. Various two-class classifiers performed excellently in case that enough abnormal examples were available (area under ROC curve A_z = 0.985 for a linear discriminant classifier). The one-class approach gave worse result when used stand-alone (A_z = 0.88 for Gaussian data description) but the combination of both approaches, using a mean combining classifier resulted in better performance when only few abnormal samples were available (average A_z = 0.94 for the combination and A_z = 0.91 for the stand-alone linear discriminant in the same set-up). This indicates that computer-aided diagnosis schemes may benefit from using a combination of two-class and one-class approaches when only few abnormal samples are available.

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