COMPARISON OF SEGMENTATION METHODS FOR DIGITAL IMAGE ANALYSIS OF CONFOCAL MICROSCOPE IMAGES TO MEASURE TRACHEID CELL DIMENSIONS

IAWA Journal ◽  
2001 ◽  
Vol 22 (3) ◽  
pp. 267-288 ◽  
Author(s):  
Mattias K. Moëll ◽  
Lloyd A. Donaldson
Keyword(s):  
Cell Wall ◽  
Image Analysis ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Transverse Cross Section ◽  
Confocal Microscope ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Cell Dimensions ◽  
Microscope Images

Image analysis is a common tool for measuring tracheid cell dimensions. When analyzing a digital image of a transverse cross section of wood, one of the initial procedures is that of segmentation. This involves classifying a picture element (pixel) as either cell wall or lumen. The accuracy of tracheid measurements is dependent on how well the result of the segmentation procedure corresponds to the true distributions of cell wall or lumen pixels. In this paper a comparison of segmentation methods is given. The effect of segmentation method on measurements is investigated and the performance of each method is discussed.We demonstrate that automated segmentation methods remove observer bias and are thus capable of more reproducible results. The contrast for confocal microscope images is of such quality that one of the fastest and simplest automatic segmentation methods may be used.

Shading Correction Methods for Digital Image Analysis of Confocal Wood Images

IAWA Journal ◽  
2007 ◽  
Vol 28 (3) ◽  
pp. 349-364 ◽  
Author(s):  
Mattias K. Moëll ◽  
Lloyd A. Donaldson
Keyword(s):  
Cell Wall ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Sample Surface ◽  
Field Of View ◽  
Wall Area ◽  
Cell Dimensions ◽  
Confocal Fluorescence ◽  
Imaging Plane

Confocal fluorescence microscopy provides a rapid method for acquiring high quality optically thin section images of wood suitable for measurement of cell dimensions. Single optical slice images of wood may occasionally contain artefacts due to differential light absorption caused by variation in the distance between the sample surface and the imaging plane across the field of view. Regional brightness variations, which we call shading, may cause problems when such images are used for wood cell measurements using digital image analysis, affecting the accuracy of wood cell dimensions. We have compared various shading correction methods for confocal microscope images and investigated the effect of shading on both the c1assification of cell wall pixels and the resulting cell dimension measurements. Severe shading results in significant errors for measurement of cell wall area, but smaller errors for cell wall thickness and lumen diameter. Some shading correction methods have unwanted effects on pixel c1assification and cell dimensions, while more effective methods remove the shading without introducing further artefacts. The effect of shading is influenced by choice of thresholding method.

Assessment of automatic segmentation accuracy with various point cloud density

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.

scholarly journals Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

2019 ◽  
Vol 9 (12) ◽  
pp. 335 ◽  
Author(s):  
Gašper Zupan ◽  
Dušan Šuput ◽  
Zvezdan Pirtošek ◽  
Andrej Vovk
Keyword(s):  
Substantia Nigra ◽  
Sensitivity And Specificity ◽  
Roc Analysis ◽  
Automatic Segmentation ◽  
High Sensitivity ◽  
Segmentation Method ◽  
Surface Sensitivity ◽  
Volume Sensitivity ◽  
Segmentation Methods ◽  
The Difference

In Parkinson’s disease (PD), there is a reduction of neuromelanin (NM) in the substantia nigra (SN). Manual quantification of the NM volume in the SN is unpractical and time-consuming; therefore, we aimed to quantify NM in the SN with a novel semi-automatic segmentation method. Twenty patients with PD and twelve healthy subjects (HC) were included in this study. T1-weighted spectral pre-saturation with inversion recovery (SPIR) images were acquired on a 3T scanner. Manual and semi-automatic atlas-free local statistics signature-based segmentations measured the surface and volume of SN, respectively. Midbrain volume (MV) was calculated to normalize the data. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of both methods. PD patients had significantly lower SN mean surface (37.7 ± 8.0 vs. 56.9 ± 6.6 mm2) and volume (235.1 ± 45.4 vs. 382.9 ± 100.5 mm3) than HC. After normalization with MV, the difference remained significant. For surface, sensitivity and specificity were 91.7 and 95 percent, respectively. For volume, sensitivity and specificity were 91.7 and 90 percent, respectively. Manual and semi-automatic segmentation methods of the SN reliably distinguished between PD patients and HC. ROC analysis shows the high sensitivity and specificity of both methods.

scholarly journals Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images

2005 ◽  
Vol 66A (1) ◽  
pp. 9-23 ◽  
Author(s):  
Gang Lin ◽  
Chris S. Bjornsson ◽  
Karen L. Smith ◽  
Muhammad-Amri Abdul-Karim ◽  
James N. Turner ◽  
...  
Keyword(s):  
Image Analysis ◽  
Neurovascular Unit ◽  
Confocal Microscope ◽  
Automated Image Analysis ◽  
Analysis Methods ◽  
Microscope Images

Research on CCD Infrared Image Threshold Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1292-1297
Author(s):  
Xing Ma ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Infrared Image ◽  
Experimental System ◽  
Segmentation Method ◽  
Gray Scale ◽  
Image Detection ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Characteristics Analysis

In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

scholarly journals Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

2020 ◽  
Author(s):  
Giulia Bertò ◽  
Daniel Bullock ◽  
Pietro Astolfi ◽  
Soichi Hayashi ◽  
Luca Zigiotto ◽  
...  
Keyword(s):  
White Matter ◽  
Surgical Planning ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Multiple Datasets ◽  
Segmentation Methods ◽  
Wide Range ◽  
Segmentation Quality ◽  
Connectivity Patterns

AbstractVirtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

Automatic segmentation method for live fish eggs microscopic image analysis

2019 ◽  
Vol 85 ◽  
pp. 49-55 ◽  
Author(s):  
Yane Duan ◽  
Daoliang Li ◽  
Lars Helge Stien ◽  
Zetian Fu ◽  
Daniel William Wright ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automatic Segmentation ◽  
Microscopic Image ◽  
Segmentation Method ◽  
Fish Eggs ◽  
Live Fish ◽  
Microscopic Image Analysis

scholarly journals An atlas-based automatic brain segmentation method for regional SUV quantification in hybrid PET/MR

2020 ◽  
Author(s):  
Weiwei Ruan ◽  
Xun Sun ◽  
Xuehan Hu ◽  
Fang Liu ◽  
Fan Hu ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Cerebral Metabolism ◽  
Automatic Segmentation ◽  
Group Analysis ◽  
Standardized Uptake Value ◽  
Emission Tomography ◽  
Manual Segmentation ◽  
Segmentation Method ◽  
Positron Emission ◽  
Segmentation Methods

Abstract Background Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation methods usually fitted subjects’ images onto an atlas template for group analysis, which changed the individuals’ images and affected regional PET segmentation. We proposed an automatic segmentation method, registering atlas template to subjects’ images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD). and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference. Results The DC of RATSI increased and the HD decreased significantly (P < 0.05) compared with the traditional method in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUVmean and SUVmax among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUVmean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P<0.05), which is consistent with previous reports.Conclusion The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation, and can be used for regional PET analysis in hybrid PET/MR.

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