scholarly journals Region-Based PDEs for Cells Counting and Segmentation in 3D+Time Images of Vertebrate Early Embryogenesis

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Barbara Rizzi ◽  
Alessandro Sarti
Keyword(s):  
Active Contour ◽  
Zebrafish Embryo ◽  
Active Contour Model ◽  
Real Data ◽  
Time Lapse ◽  
Cell Nuclei ◽  
Qualitative And Quantitative ◽  
Different Shapes ◽  
Zebrafish Embryogenesis ◽  
Microscopy Images

This paper is devoted to the segmentation of cell nuclei from time lapse confocal microscopy images, taken throughout early Zebrafish embryogenesis. The segmentation allows to identify and quantify the number of cells in the animal model. This kind of information is relevant to estimate important biological parameters such as the cell proliferation rate in time and space. Our approach is based on the active contour model without edges. We compare two different formulations of the model equation and evaluate their performances in segmenting nuclei of different shapes and sizes. Qualitative and quantitative comparisons are performed on both synthetic and real data, by means of suitable gold standard. The best approach is then applied on a number of time lapses for the segmentation and counting of cells during the development of a zebrafish embryo between the sphere and the shield stage.

scholarly journals 3D Cell Image Segmentation by Modified Subjective Surface Method

2020 ◽  
Vol 75 (1) ◽  
pp. 147-162
Author(s):  
MarKjoe Olunna Uba ◽  
Karol Mikula ◽  
Zuzana Krivá ◽  
Hanh Nguyen ◽  
Thierry Savy ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Real Data ◽  
Finite Volume Scheme ◽  
3D Image ◽  
Cell Nuclei ◽  
Surface Evolution ◽  
Surface Method ◽  
Segmentation Approach ◽  
Level Set Formulation ◽  
Microscopy Images

AbstractIn this work, we focused on 3D image segmentation where the segmented surface is reconstructed by the use of 3D digital image information and information from thresholded 3D image in a local neighbourhood. To this end, we applied a mathematical model based on the level set formulation and numerical method which is based on the so-called reduced diamond cell approach. The segmentation approach is based on surface evolution governed by a nonlinear PDE, the modified subjective surface equation. This is done by defining the input to the edge detector function as the weighted sum of norm of presmoothed 3D image and norm of presmoothed thresholded 3D image in a local neighbourhood. For the numerical discretization, we used a semi-implicit finite volume scheme. The method was applied to real data representing 3D microscopy images of cell nuclei within the zebrafish pectoral fin.

A SEGMENTATION FRAMEWORK FOR PHASE CONTRAST AND FLUORESCENCE MICROSCOPY IMAGES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460013 ◽  
Author(s):  
MITCHEL ALIOSCHA-PEREZ ◽  
RONNIE WILLAERT ◽  
HICHEM SAHLI
Keyword(s):  
Active Contour ◽  
Phase Contrast ◽  
Active Contour Model ◽  
Region Of Interest ◽  
Energy Functional ◽  
Living Cells ◽  
Shape Fitting ◽  
Contour Model ◽  
Microscopy Images

The noninvasive imaging of unstained living cells is a widely used technique in biotechnology for determining biological and biochemical role of proteins, since it allows studying living specimens without altering them. Usually, fluorescence and contrast (or transmission) images are both used complementarily, as their combination allows possible better outcomes. However, segmentation of contrast images is particularly difficult due to the presence of defocused scans, lighting/shade-off artifacts and cells overlapping. In this work, we investigate the optical properties intervening during the image formation process, and propose different segmentation strategies that can benefit from these properties. The proposed scheme (i) combines the estimated phase and the fluorescence information in order to obtain initial markers for a latter segmentation stage; and (ii) use the shear oriented polar snakes, an active contour model that implicitly involves phase information on its energy functional. The obtained contour can be used as region of interest estimation, as data for a latter shape-fitting process, or as smart markers for a more detailed segmentation process (i.e. watershed). Experimental results provide a comparison of the different segmentation schemes, and confirm the suitability of the proposed strategy and model for cell images segmentation.

Energy Update Restricted Chan–Vese Model for Tumor Extraction from MRI of Human Head Scans

2017 ◽  
Vol 15 (01) ◽  
pp. 1750081
Author(s):  
T. Kalaiselvi ◽  
S. Karthigai Selvi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Active Contour ◽  
Active Contour Model ◽  
Human Head ◽  
Multiphase Model ◽  
Resonance Imaging ◽  
Qualitative And Quantitative ◽  
Contour Model ◽  
Tumor Extraction

This research paper proposed a newer strategic method for the extraction of tumor from magnetic resonance imaging scans by employing a region-based active contour model (ACM). The earlier methods have applied the process of contour initialization randomly and updating the energy of the contour at every iteration. The proposed method used wavelet-based feature set to initiate the contour and restricts the energy update procedure. The efficiency of the presented technique in terms of tumor extraction is measured through qualitative and quantitative measures further compared with its counterparts Vese–Chan multiphase model, ACM and Selective binary. Gaussian filtering regularized level set and non active contour based models.

scholarly journals Cell Tracking using Coupled Active Surfaces for Nuclei and Membranes

2009 ◽  
Author(s):  
Kishore Mosaliganti ◽  
Benjamin Smith ◽  
Arnaud Gelas ◽  
Alexandre Gouaillard ◽  
Sean Megason
Keyword(s):  
Cell Tracking ◽  
Zebrafish Embryo ◽  
Active Contours ◽  
Time Lapse ◽  
Constant Intensity ◽  
Real Time Tracking ◽  
Tracking Filter ◽  
Microscopy Images ◽  
Processing Framework ◽  
Time Point

An Insight Toolkit (ITK) processing framework for segmenting and tracking nuclei in time-lapse microscopy images using coupled active contours is presented in this paper. We implement the method of Dufour et al.[2] to segment and track cells in fluorescence microscopy images. The basic idea is to model the image as a constant intensity background with constant intensity foreground components. We utilizes our earlier submissions on the Chan and Vese algorithm [1] and its multiphase extension [5] to build our new tracking filter. The tracking filter itk::MultiphaseLevelSetTracking inputs a segmentation result (or a coarse estimate) from the previous time-point along with the feature image and generates a new segmentation output. By iteratively repeating this process across all time-points, real-time tracking is made possible. We include 2D/3D example code, parameter settings and show the results generated on a 2D zebrafish embryo image series.

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