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.

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 A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Biomedical Image Segmentation Using Spatial Kernel Fuzzy C-Means Based Level Set Formulation

2012 ◽  
Vol 2 (2) ◽  
pp. 200-205
Author(s):  
Raghotham Reddy Ganta ◽  
Syed Zaheeruddin ◽  
Narsimha Baddiri ◽  
R. Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Biomedical Image ◽  
Level Set Formulation

A Hybrid Image Segmentation Approach for Thermal Barrier Coating Quality Assessments

2021 ◽  
Author(s):  
Zanyah Ailsworth ◽  
Wei-Bang Chen ◽  
Yongjin Lu ◽  
Xiaoliang Wang ◽  
Melissa Tsui ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Coating Quality ◽  
Barrier Coating ◽  
Segmentation Approach ◽  
Quality Assessments

scholarly journals An Image Segmentation Method Based on Improved Regularized Level Set Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2393 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
External Energy ◽  
Regularization Term ◽  
Level Set Function ◽  
New Energy ◽  
Segmentation Approach ◽  
Set Function ◽  
Distance Regularization

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

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