Segmentation of Meristem Cells by an Automated Optimization Algorithm

Meristem cells are irregularly shaped and appear in confocal images as dark areas surrounded by bright ones. Images are characterized by regions of very low contrast and absolute loss of edges deeper into the meristem. Edges are blurred, discontinuous, sometimes indistinguishable, and the intensity level inside the cells is similar to the background of the image. Recently, a technique called Parametric Segmentation Tuning was introduced for the optimization of segmentation parameters in diatom images. This paper presents a PST-tuned automatic segmentation method of meristem cells in microscopy images based on mathematical morphology. The optimal parameters of the algorithm are found by means of an iterative process that compares the segmented images obtained by successive variations of the parameters. Then, an optimization function is used to determine which pair of successive images allows for the best segmentation. The technique was validated by comparing its results with those obtained by a level set algorithm and a balloon segmentation technique. The outcomes show that our methodology offers better results than two free available state-of-the-art alternatives, being superior in all cases studied, losing 9.09% of the cells in the worst situation, against 75.81 and 25.45 obtained in the level set and the balloon segmentation techniques, respectively. The optimization method can be employed to tune the parameters of other meristem segmentation methods.

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Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

Methods of Information in Medicine ◽

10.3414/me11-02-0037 ◽

2012 ◽

Vol 51 (05) ◽

pp. 415-422 ◽

Cited By ~ 8

Author(s):

A. Schmidt-Richberg ◽

J. Fiehler ◽

T. Illies ◽

D. Möller ◽

H. Handels ◽

...

Keyword(s):

Level Set ◽

Automatic Segmentation ◽

Time Of Flight ◽

Medical Expert ◽

Consistency Check ◽

Major Drawback ◽

Segmentation Methods ◽

Initial Segmentation ◽

Level Set Approach ◽

Manual Correction

Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

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Assessment of automatic segmentation accuracy with various point cloud density

Geodesy and Cartography ◽

10.22389/0016-7126-2020-961-7-47-55 ◽

2020 ◽

Vol 961 (7) ◽

pp. 47-55

Author(s):

A.G. Yunusov ◽

A.J. Jdeed ◽

N.S. Begliarov ◽

M.A. Elshewy

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Reference Data ◽

Automatic Segmentation ◽

Geometric Accuracy ◽

Segmentation Method ◽

Segmentation Methods ◽

Segmentation Accuracy ◽

Number Of Segments ◽

Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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Multi-material plastic part design via the level set shape and topology optimization method

Engineering Optimization ◽

10.1080/0305215x.2016.1141203 ◽

2016 ◽

Vol 48 (11) ◽

pp. 1910-1931 ◽

Cited By ~ 2

Author(s):

Jikai Liu ◽

Kajsa Duke ◽

Yongsheng Ma

Keyword(s):

Topology Optimization ◽

Level Set ◽

Optimization Method ◽

Plastic Part ◽

Shape And Topology Optimization ◽

And Topology ◽

Plastic Part Design ◽

Topology Optimization Method

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A versatile algorithm for the automatic segmentation of hippocampus based on level set

International Journal of Biomedical Engineering and Technology ◽

10.1504/ijbet.2011.043295 ◽

2011 ◽

Vol 7 (3) ◽

pp. 213 ◽

Cited By ~ 1

Author(s):

J. Rajeesh ◽

R.S. Moni ◽

S. Palanikumar ◽

T. Gopalakrishnan

Keyword(s):

Level Set ◽

Automatic Segmentation

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Simplified Modeling Research of Reinforced Concrete Based on the Dynamic Stiffness Equivalence

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.446-449.458 ◽

2012 ◽

Vol 446-449 ◽

pp. 458-462

Author(s):

Jie Hu ◽

Jia Quan Feng ◽

Xi Nong Zhang

Keyword(s):

Reinforced Concrete ◽

Reference Model ◽

Dynamic Stiffness ◽

Optimization Method ◽

Simplified Model ◽

Response Analysis ◽

Reinforced Concrete Structure ◽

Optimization Function ◽

Nature Frequency ◽

Simplified Modeling

This paper proposed a simplified modeling method of reinforced concrete based on the equivalence of dynamic stiffness, the parameters of simplified model were modified to make the error of nature frequency between reference model and simplified model as small as possible, and an appropriate optimization function was designed. The essentiality of the proposed method is parameter optimization, with the advantages such as fewer elements and calculation assumption. The numerical simulation result indicated that this optimization method is suitable for the dynamic response analysis of complicated reinforced concrete structure.

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Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

Journal of Medical Engineering ◽

10.1155/2015/457906 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 65

Author(s):

Rajesh Kumar ◽

Rajeev Srivastava ◽

Subodh Srivastava

Keyword(s):

Texture Features ◽

Histogram Equalization ◽

Gray Level ◽

Segmentation Methods ◽

Segmented Images ◽

Clinically Significant ◽

Classification Of Images ◽

Nearest Neighborhood ◽

Neighborhood Method

A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

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A Structural Optimization Method Incorporating Level Set Boundary Expressions Based on the Concept of the Phase Field Method

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series A ◽

10.1299/kikaia.75.550 ◽

2009 ◽

Vol 75 (753) ◽

pp. 550-558 ◽

Cited By ~ 11

Author(s):

Takayuki YAMADA ◽

Shinji NISHIWAKI ◽

Kazuhiro IZUI ◽

Masataka YOSHIMURA ◽

Akihiro TAKEZAWA

Keyword(s):

Structural Optimization ◽

Level Set ◽

Phase Field ◽

Optimization Method ◽

Field Method ◽

Phase Field Method

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Level set-based BEM topology optimization method for maximizing total potential energy of thermal problems

International Journal of Heat and Mass Transfer ◽

10.1016/j.ijheatmasstransfer.2021.121921 ◽

2022 ◽

Vol 182 ◽

pp. 121921

Author(s):

Guoxian Jing ◽

Jiao Jia ◽

Jiawei Xiang

Keyword(s):

Topology Optimization ◽

Potential Energy ◽

Level Set ◽

Optimization Method ◽

Total Potential Energy ◽

Total Potential ◽

Topology Optimization Method

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SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237215500477 ◽

2015 ◽

Vol 27 (05) ◽

pp. 1550047 ◽

Cited By ~ 2

Author(s):

Gaurav Sethi ◽

B. S. Saini

Keyword(s):

Level Set ◽

Active Contour ◽

Active Contour Model ◽

Diagnostic System ◽

Ct Images ◽

Active Contour Models ◽

Segmentation Methods ◽

Contour Models ◽

Edge Based ◽

Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

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