4,650 research outputs found
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
The poor contrast and the overlapping of cervical cell cytoplasm are the
major issues in the accurate segmentation of cervical cell cytoplasm. This
paper presents an automated unsupervised cytoplasm segmentation approach which
can effectively find the cytoplasm boundaries in overlapping cells. The
proposed approach first segments the cell clumps from the cervical smear image
and detects the nuclei in each cell clump. A modified Otsu method with prior
class probability is proposed for accurate segmentation of nuclei from the cell
clumps. Using distance regularized level set evolution, the contour around each
nucleus is evolved until it reaches the cytoplasm boundaries. Promising results
were obtained by experimenting on ISBI 2015 challenge dataset.Comment: 4 pages, 4 figures, Biomedical Engineering and Sciences (IECBES),
2016 IEEE EMBS Conference on. IEEE, 201
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
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