A semi-automatic method for robust and efficient identification of neighboring muscle cells

Wang, Zhenzhou

doi:10.1016/j.patcog.2015.12.009

Cited by 32 publications

(56 citation statements)

References 37 publications

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“…It is indespensable for the proposed approach to achieve high accuracy because this threshold selection method breaks through the bottleneck of segmentation accuracy as stated and testified in Refs. [36,37].…”

Section: Discussionmentioning

confidence: 99%

“…A correspondence trajectory is drawn between each sampled point and the centroid of the smallest ventricle boundary using a mapping defined by the gradient field of the solution of the Laplace equation. 37 To make sure that the correspondence trajectories can intersect all the segmented boundaries in the same slice, it is extended outward from the sampled points based on the slope of the trajectory at the sampled points. The intersections of the trajectories with the tracked boundaries are denoted as P Figure 10 shows the plotted trajectories for one slice and intersection points for one frame both with the tracked boundary and the manual boundary.…”

Section: Validation Methodsmentioning

confidence: 99%

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A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

Wang¹

2016

j med imaging hlth inform

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Identification of the endocardial borders remains challenging in cardiology. In this paper, we propose a new approach named 'deformation flow tracking' which couples the obtained boundary from the previous frame and the extracted edges in the current frame by energy minimization. Firstly, the edges are extracted accurately by an effective threshold selection method. Then, the boundary in the previous frame is driven toward the extracted edges to form a deformation boundary by minimizing the energy between the deformation boundary and extracted edge while keeping the deformation boundary smooth. Deformation thresholds are defined and used to constrain the motions of the boundary points and eliminate outliers effectively. The proposed approach was tested on complete short-axis cine MRI datasets from 5 normal subjects and 5 patients with heart failure (total of 1660 images) randomly chosen from a much larger dataset (100 cases). As it turned out, the proposed approach is efficient and robust for automatic identification of the ventricular endocardial boundary that moves directionally and regularly, which is true in most cases.

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Section: Discussionmentioning

confidence: 99%

Section: Validation Methodsmentioning

confidence: 99%

A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

Wang¹

2016

j med imaging hlth inform

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“…Different methods were proposed and claimed to be superior in segmenting a class of cells. These methods include watershed method [10–12], region growing based method [13], morphological method [14, 15], clustering based method [16], contour based method [17], multilayer segmentation based method [18], pattern modeling based method [19], supervised learning method [20], morphological watershed based method [21], inference based method [22] and methods that combine the threshold selection and morphology techniques [23–25]. However, the performance and applicability of most of these methods are very limited because they are diverging rather than convergent to a generalized solution to address so many types of cells.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this drawback, the author has proposed a new approach to segment and quantify different types of cells or nanoparticles based on the general property of the cell images: global intensity distribution and local gradient [24], which is more versatile than the referenced state of the art methods. The approach proposed in [24] evolves the method proposed in [25] and makes it to be able to segment and quantify more types of cells or nanoparticles. One fundamental improvement of [24] compared to [25] is that the threshold selection method used in [25] was improved to be able to segment more types of cells or nanoparticles robustly.…”

Section: Introductionmentioning

confidence: 99%

“…The approach proposed in [24] evolves the method proposed in [25] and makes it to be able to segment and quantify more types of cells or nanoparticles. One fundamental improvement of [24] compared to [25] is that the threshold selection method used in [25] was improved to be able to segment more types of cells or nanoparticles robustly. However, the details of how to apply the proposed threshold selection method with practical cell images are not addressed adequately in [24].…”

Section: Introductionmentioning

confidence: 99%

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Generalizing cell segmentation and quantification

Wang

2017

BMC Bioinformatics

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BackgroundIn recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.ResultsTo make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.ConclusionsThe proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.

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A semi-automatic method for robust and efficient identification of neighboring muscle cells

Cited by 32 publications

References 37 publications

A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

A New Approach for Automatic Identification of the Ventricular Boundary in Cine Magnetic Resonance Images

Generalizing cell segmentation and quantification

Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges

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