• Toxicological Research
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• v.18 no.4
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• pp.369-374
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• 2002

The present study was performed to validate an automated image analysis system (Loats Automated Micronucleus Scoring System) for the mouse bone marrow micronucleus assay, comparing with conventional microscopic scoring. Two studies were conducted to provide slides for a comparison of micro-nucleated polychromatic erythrocytes (MNPCEs) values collected manually to those collected by the auto-mated system. Test article A was used as an example of a compound negative for the induction of micronuclei and test article B was wed as a micronucleus-inducing agent to elicit a positive response. Cyclophosphamide was included to provide an positive control in two studies. Bone marrow samples were collected 24 h after administration of test article A and B in male ICR mice. The cells were fixed with absolute methanol and stained with May-Grunwald and Giemsa. The number of MNPCEs was determined by the analysis of 1000 total PCEs per bone marrow sample. In addition to micronucleus scoring, an index of bone marrow toxicity based on PCE ratio (% of PCEs to total erythrocytes) was determined for each sample. The automated and manual scoring was similar when the MNPCEs incidence induced by each test article was less than 10. However manual scoring was able to effectively enumerate micronucleated PCEs in mouse bone marrow when MNPCEs incidence was more than 10, such as cyclophosphamide treatment. Conversely, PCE ratio was superior in computer-assisted image analysis. Taken together, it is suggested that improvement of the automated image analysis may be necessary to render the automatic scoring as sensitive as manual scoring for routine counting of micronuclei, especially because it is superior in objectivity and high throughput scoring.

• The Journal of Advanced Prosthodontics
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• v.10 no.4
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• pp.279-285
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• 2018

PURPOSE. The aim of this clinical study was to assess the accuracy of the implants placed using a universal digital surgical guide. MATERIALS AND METHODS. Among 17 patients, 28 posterior implants were included in this study. The digital image of the soft tissue acquired from cast scan and hard tissue from CBCT have been superimposed and planned the location, length, diameter of the implant fixture. Then digital surgical guides were created using 3D printer. Each of angle deviations, coronal, apical, depth deviations of planned and actually placed implants were calculated using CBCT scans and casts. To compare implant positioning errors by CBCT scans and plaster casts, data were analyzed with independent samples t-test. RESULTS. The results of the implant positioning errors calculated by CBCT and casts were as follows. The means for CBCT analyses were: angle deviation: $4.74{\pm}2.06^{\circ}$, coronal deviation: $1.37{\pm}0.80mm$, and apical deviation: $1.77{\pm}0.86mm$. The means for cast analyses were: angle deviation: $2.43{\pm}1.13^{\circ}$, coronal deviation: $0.82{\pm}0.44mm$, apical deviation: $1.19{\pm}0.46mm$, and depth deviation: $0.03{\pm}0.65mm$. There were statistically significant differences between the deviations of CBCT scans and cast. CONCLUSION. The model analysis showed lower deviation value comparing the CBCT analysis. The angle and length deviation value of the universal digital guide stent were accepted clinically.

• Clinical and Experimental Reproductive Medicine
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• v.38 no.1
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• pp.47-52
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• 2011

Objective: To determine whether characteristics of sperm motility obtained by computer-assisted sperm analysis (CASA) could predict pregnancy after intrauterine insemination (IUI) in couples with unexplained infertility. Methods: Three hundred eighty-three cycles of intrauterine insemination with superovulation were retrospectively analyzed. Semen analysis was performed with CASA before and after swim-up and the parameters were compared between pregnant and non-pregnant women. Results: The pregnancy rate per cycle was 14.1%. Pregnant and non-pregnant women were comparable in terms of age, infertility duration, the number of dominant follicles. While sperm concentration, motility, and parameters such as average path velocity (VAP) and percentage rapid (RAPID) before semen preparation were significantly different between the pregnancy and non-pregnancy groups, there were no differences in sperm parameters when comparing the two groups after preparation. Using a receiver operating characteristic curve to measure sensitivity and specificity, the optimal threshold value for the predictors of pregnancy was revealed to be a concentration of ${\geq}111{\times}10^6/mL$, a motility of ${\geq}$ 51.4%, and RAPID ${\geq}$ 30.1% before preparation for IUI. Conclusion: Sperm parameters including concentration, motility, and RAPID before sperm preparation could have predictive value for pregnancy outcome after intrauterine insemination with superovulation in couples with unexplained infertility, and would be helpful when counseling patients before they make the decision to proceed with IVF/ICSI-ET.

• Applied Microscopy
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• v.51
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• pp.6.1-6.9
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• 2021

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Computers and Concrete
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• v.5 no.2
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• pp.101-116
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• 2008

The microscopic determination of air void characteristics in hardened concrete, defined in EN 480-11 as the linear-traverse method, is an extremely time-consuming and tedious task. Over past decades, several researchers have proposed relatively expensive mechanical automated systems which could replace the human operator in this procedure. Recently, the appearance of new high-resolution flatbed scanners has made it possible for the procedure to be automated in a fully-computerized and thus cost-effective way. The results of our work indicate the high sensitivity of such image analysis automated systems firstly to the quality of sample surface preparation, secondly to the selection of the air void threshold value, and finally to the selection of the probe system. However, it can be concluded that in case of careful validation and the use of the approach which is proposed in the paper, such automated systems can give very good estimate of the air void system parameters, defined in EN 480-11. The amount of time saved by using such a procedure is immense, and there is also the possibility of using alternative stereological methods to assess other, perhaps also important, characteristics of air void system in hardened concrete.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.57-69
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• 2006

For decades, scientists and clinicians have examined methods of measuring scalp hair growth. There has been a greater need for reliable, economical and minimally invasive means of measuring hair growth and, specifically, response to Oriental medicine therapy. We review the various methods of measurement described to date, their limitations and value to the clinician. In our opinion, the potential of computer-assisted technology in this field is yet to be maximized and the currently available tools are less than ideal. The most valuable means of measurement at the present time are global photography and phototrichogram-based techniques (with digital image analysis). Subjective scoring systems are also of value in the overall assessment of response to therapy and these are under-utilized and merit further refinement.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.134-144
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• 2006

Tongue diagnosis is an important diagnostic method in traditional Oriental medicine. It has been especially accepted that quantitative analysis of tongue images allows the accurate diagnosis of the exterior-interior and cold-heat patterns of a patient. However, to ensure stable and reliable results, the color reproduction of such images must first be error-tree. Moreover, tongue diagnosis is much influenced by the surrounding illumination and subjective color recognition, so it has to be performed objectively and quantitatively using a digital diagnostic machine. In this study, 457 tongue images of outpatients were collected using the Digital Tongue Inspection System. Through statistical analysis, the result shows that the heat and cold patterns can be distinguished clearly based on the hue value of the tongue images. The average hue value (1.00) of the tongue's image in the cold pattern is higher than that in the heat pattern (0.99).