2,198 research outputs found
Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs
This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98
Red blood cell segmentation and classification method using MATLAB
Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very
important process for early detection of related disease such as malaria and anemia before
suitable follow up treatment can be proceed. Some of the human disease can be showed
by counting the number of red blood cells. Red blood cell count gives the vital information
that help diagnosis many of the patient’s sickness. Conventional method under blood
smears RBC diagnosis is applying light microscope conducted by pathologist. This
method is time-consuming and laborious. In this project an automated RBC counting is
proposed to speed up the time consumption and to reduce the potential of the wrongly
identified RBC. Initially the RBC goes for image pre-processing which involved global
thresholding. Then it continues with RBCs counting by using two different algorithms
which are the watershed segmentation based on distance transform, and the second one is
the artificial neural network (ANN) classification with fitting application depend on
regression method. Before applying ANN classification there are step needed to get
feature extraction data that are the data extraction using moment invariant. There are still
weaknesses and constraints due to the image itself such as color similarity, weak edge
boundary, overlapping condition, and image quality. Thus, more study must be done to
handle those matters to produce strong analysis approach for medical diagnosis purpose.
This project build a better solution and help to improve the current methods so that it can
be more capable, robust, and effective whenever any sample of blood cell is analyzed. At
the end of this project it conducted comparison between 20 images of blood samples taken
from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM).
The proposed method has been tested on blood cell images and the effectiveness and
reliability of each of the counting method has been demonstrated
Automated characterization of cell shape changes during amoeboid motility by skeletonization
<p>Abstract</p> <p>Background</p> <p>The ability of a cell to change shape is crucial for the proper function of many cellular processes, including cell migration. One type of cell migration, referred to as amoeboid motility, involves alternating cycles of morphological expansion and retraction. Traditionally, this process has been characterized by a number of parameters providing global information about shape changes, which are insufficient to distinguish phenotypes based on local pseudopodial activities that typify amoeboid motility.</p> <p>Results</p> <p>We developed a method that automatically detects and characterizes pseudopodial behavior of cells. The method uses skeletonization, a technique from morphological image processing to reduce a shape into a series of connected lines. It involves a series of automatic algorithms including image segmentation, boundary smoothing, skeletonization and branch pruning, and takes into account the cell shape changes between successive frames to detect protrusion and retraction activities. In addition, the activities are clustered into different groups, each representing the protruding and retracting history of an individual pseudopod.</p> <p>Conclusions</p> <p>We illustrate the algorithms on movies of chemotaxing <it>Dictyostelium </it>cells and show that our method makes it possible to capture the spatial and temporal dynamics as well as the stochastic features of the pseudopodial behavior. Thus, the method provides a powerful tool for investigating amoeboid motility.</p
Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
Cell segmentation in microscopy is a challenging problem, since cells are
often asymmetric and densely packed. This becomes particularly challenging for
extremely large images, since manual intervention and processing time can make
segmentation intractable. In this paper, we present an efficient and highly
parallel formulation for symmetric three-dimensional (3D) contour evolution
that extends previous work on fast two-dimensional active contours. We provide
a formulation for optimization on 3D images, as well as a strategy for
accelerating computation on consumer graphics hardware. The proposed software
takes advantage of Monte-Carlo sampling schemes in order to speed up
convergence and reduce thread divergence. Experimental results show that this
method provides superior performance for large 2D and 3D cell segmentation
tasks when compared to existing methods on large 3D brain images
ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.
Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.This work has been partially supported by the National Grant
TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness, Madrid Regional Government and Universidad Carlos
III de Madrid through the project SHARON-CM-UC3M, RTI2018-
095497-B-I00 from Ministerio de Ciencia e Innovación (MICINN)
and HR17_00527 from Fundación La Caixa to A.H. M.M-M. is supported by the Spanish Ministry of Education, Culture and Sports
FPU Grant FPU18/02825. M.P-S. is supported by a Federation of European Biochemical Societies long-term fellowship. J.S. is supported
by a fellowship (PRE2019-089130) from MICINN.S
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
Adaptive Re-Segmentation Strategies for Accurate Bright Field Cell Tracking
Understanding complex interactions in cellular systems requires accurate tracking of individual cells observed in microscopic image sequence and acquired from multi-day in vitro experiments. To be effective, methods must follow each cell through the whole experimental sequence to recognize significant phenotypic transitions, such as mitosis, chemotaxis, apoptosis, and cell/cell interactions, and to detect the effect of cell treatments. However, high accuracy long-range cell tracking is difficult because the collection and detection of cells in images is error-prone, and single error in a one frame can cause a tracked cell to be lost. Detection of cells is especially difficult when using bright field microscopy images wherein the contrast difference between the cells and the background is very low. This work introduces a new method that automatically identifies and then corrects tracking errors using a combination of combinatorial registration, flow constraints, and image segmentation repair
Robust active contour segmentation with an efficient global optimizer
Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed, combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information, but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for the proposed active contour method. It is experimentally shown that the proposed method has significant better results in the presence of noise and clutter
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