Evaluation of ImageJ software in ultrasonic image analysis: follicular and luteal morphological characteristics of cattle

Ultrasonic images of the kidney and of the liver are subjected to a multiscale analysis in a generalized Hermite pyramid framework. The gradient images of the multiscale decompositions of the images of healthy and sick kidneys, and of the intraoperative and conventionally imaged livers, exhibit differences, in the structures of gray level regions, which can be interpreted by a medical doctor. These are used as inputs to an unsupervised classifier to automatically classify the images into homogeneous groups, which are found, in the case of the ultrasonic images examined, to correspond to the different physical characteristics of tissues of the organs under study. The main contribution of the paper is believed to be the multiscale tissue characterization along with its display in a manner that has utility as a diagnostic aid to the clinician.

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Image Analysis and Functional Data Clustering for Random Shape Aggregate Models

Mathematics ◽

10.3390/math8111903 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1903

Author(s):

Jonghyun Yun ◽

Sanggoo Kang ◽

Amin Darabnoush Tehrani ◽

Suyun Ham

Keyword(s):

Image Analysis ◽

Mixture Model ◽

Functional Data ◽

Data Clustering ◽

Learning Algorithm ◽

Morphological Characteristics ◽

Random Shape ◽

Aggregate Model ◽

Random Aggregate ◽

Aggregate Morphology

This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.

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Ultrasonic image analysis for beef tenderness

10.1117/12.144021 ◽

1993 ◽

Cited By ~ 6

Author(s):

Bosoon Park ◽

Brian R. Thane ◽

A. D. Whittaker

Keyword(s):

Image Analysis ◽

Beef Tenderness ◽

Ultrasonic Image

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IMAGE ANALYSIS COMBINED FLUORESCENCE MICROSCOPY - Examples of ImageJ Software Application in Yeast Studies

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms ◽

10.5220/0003120902580261 ◽

2011 ◽

Keyword(s):

Image Analysis ◽

Fluorescence Microscopy ◽

Software Application ◽

Imagej Software

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Diagnosis of Hepatic Fibrosis by Ultrasonic Image Analysis

2012 International Conference on Biomedical Engineering and Biotechnology ◽

10.1109/icbeb.2012.143 ◽

2012 ◽

Cited By ~ 2

Author(s):

Guohui Zhou ◽

Yuanyuan Wang ◽

Weiqi Wang

Keyword(s):

Image Analysis ◽

Hepatic Fibrosis ◽

Ultrasonic Image

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Neuronal expression of Fas-associated death domain protein and caspase-8 in the perinidal parenchyma of cerebral arteriovenous malformations

Journal of Neurosurgery ◽

10.3171/jns.2007.106.2.275 ◽

2007 ◽

Vol 106 (2) ◽

pp. 275-282 ◽

Cited By ~ 8

Author(s):

Yasushi Takagi ◽

Ken-Ichiro Kikuta ◽

Kazuhiko Nozaki ◽

Motoaki Fujimoto ◽

Junya Hayashi ◽

...

Keyword(s):

Image Analysis ◽

Arteriovenous Malformations ◽

Morphological Characteristics ◽

Terminal Deoxynucleotidyl Transferase ◽

Caspase 8 ◽

Image Analysis System ◽

Death Domain ◽

Cerebral Arteriovenous Malformations ◽

Domain Protein ◽

Analysis System

Object The expression and localization of phosphorylated Fas-associated death domain protein (pFADD) and cleaved caspase-8 was examined in human cerebral arteriovenous malformations (AVMs). The authors focused on the perinidal parenchyma to clarify the effect of AVMs on perinidal brain tissue. Methods Seventeen cerebral AVMs were analyzed using immunohistochemical methods. Specimens were removed from patients during surgical procedures. The characteristics of the areas that stained positively for pFADD or cleaved caspase-8 were also assessed using an image analysis system. Eleven (65%) of the 17 lesions demonstrated anti-pFADD immunoreactivity and 12 (71%) showed anti–cleaved caspase-8 immunoreactivity. The immunoreactive cells in the perinidal parenchyma demonstrated obvious neuronal morphological characteristics. The characteristics of pFADD-positive and cleaved caspase-8–positive areas were assessed using the image analysis system. The mean distance from the nidus adjacent to either area was not affected by preoperative hemorrhage. The neuronal densities of pFADD-positive and cleaved caspase-8–positive areas were analyzed using the same system. The density of the control area (samples that were pFADD-negative and cleaved caspase-8 negative) was significantly higher when compared with that of pFADD-positive and cleaved caspase-8–positive areas (p < 0.05). The expressions of cleaved caspase-9, cleaved poly(adenosine diphosphate–ribose) polymerase, and apoptotic cells were analyzed using the terminal deoxynucleotidyl transferase–mediated deoxyuridine triphosphate nick-end labeling method. Conclusions Neuronal areas that stained positively for pFADD and cleaved caspase-8 existed around the nidus of AVMs. In these areas, the neuronal density was lower than that in the other parenchyma around the AVM. Neuronal loss around the nidus may be the origin of brain dysfunction around AVMs.

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Image Analysis Evaluation of Aggregates for Asphalt Concrete Mixtures

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1615-09 ◽

1998 ◽

Vol 1615 (1) ◽

pp. 65-71 ◽

Cited By ~ 16

Author(s):

Chun-Yi Kuo ◽

Reed B. Freeman

Keyword(s):

Image Analysis ◽

Asphalt Concrete ◽

Morphological Characteristics ◽

Engineering Properties ◽

Natural Sand ◽

Coarse Aggregates ◽

Concrete Mixtures ◽

Fine Aggregates ◽

Pavement Engineering ◽

Recent Developments

The performance of asphalt concrete mixtures is influenced by the properties of the included aggregates, such as grading, shape (angularity and elongation), and texture (roughness). Complete and accurate quantification of aggregate properties is essential for understanding their influence on asphalt concrete and for selecting aggregates to produce high-quality paving mixtures. Recent developments in the use of digital image analysis techniques for quantifying aggregate morphological characteristics in asphalt concrete are summarized. Image morphological characteristics were used to quantify flatness and elongation of coarse aggregates, to estimate the proportion of natural sand in fine aggregates, and to correlate aggregate characteristics with engineering properties of asphalt concrete mixtures. Image analysis of sections also revealed information about the grading, shape, and orientation of coarse aggregates in a mixture. An overview is presented of the broad range of useful pavement engineering applications of this relatively new approach for evaluating aggregate characteristics.

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A Generalized Hermite Pyramid for Ultrasonic Image Analysis I. Kidney and Liver

Ultrasonic Imaging ◽

10.1006/uimg.1996.0015 ◽

1996 ◽

Vol 18 (4) ◽

pp. 261-304 ◽

Cited By ~ 2

Author(s):

Y Venkatesh

Keyword(s):

Image Analysis ◽

Ultrasonic Image

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Estimation and analysis of particles from seeds of maximiliana maripa (aubl.) drude (arecaceae)

Scientific Electronic Archives ◽

10.36560/131020201056 ◽

2020 ◽

Vol 13 (10) ◽

pp. 28

Author(s):

C. B. M. Farias ◽

A. Arrolho ◽

M. C. M. Silva ◽

R. R. Cruz ◽

L. P. N. Ramos ◽

...

Keyword(s):

Image Analysis ◽

Digital Image ◽

Evaluation Method ◽

High Dispersion ◽

Seed Length ◽

Automatic Mode ◽

Low Correlation ◽

Imagej Software ◽

Maximiliana Maripa ◽

Digital Caliper

The present work aims to estimate the length and width of the seeds, through the analysis of digital images and to validate the methodology through statistical data. To estimate the length and width of seeds via image analysis, 50 M.maripa seeds were used. The seeds were arranged in a decreasing way from 1 to 25 diagonally, on a matte black fabric, on a phenotyping platform, with a Sony Hd Avchd progressive digital Gps camera, coupled at a height of 50 cm. The images were captured by the camera in automatic mode, without flash, automatic ISO speed, in an RGB system and with a size above 2Mb. Soon after the seeds passed the traditional evaluation method with the aid of a digital caliper, measured in terms of length and width. The images were analyzed with the ImageJ software. Statistical analyzes were performed with the aid of the Sigmaplot program. The results of the length and width of the seeds of Inajá seen by the caliper and digital image via camera, were very distant, showing high dispersion and low correlation r = 0.4037 and R² = 0.1629 for length and r = 0.2985 and R² = 0.0891, showing that the Compared data had little similarity. The error between both the methodology was considered low: 3,81063 and 3,769 for the variables of seed length and width. The method of analysis by digital image and caliper for estimating the length and width of Maximiliana maripa seeds showed a low correlation between the two methodologies. The use of image analysis to estimate the length and width of M. maripa seeds is not indicated.

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