• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2352-2355
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• 2003

In recent yeas, studies on computer aided diagnosis (CAD) using image analysis on CT images have been conducted with respect to various diseases. Extracting ground-glass opacities (GGO) on lung CT images is one of such subjects, though it has not found an established method yet. If the region of ground-glass opacities is large on CT images, it can be detected without much difficulty. On the other hand, if the region is small, it is still difficult to find it exactly. In the latter case, increasing overlooking possibility cannot be avoided according to smaller size of the region. To solve this difficulty, this paper proposes an automatic technique for extracting ground-glass opacities on lung CT images employing some statistical parameters of a gray level histogram and a differential histogram. The proposed technique is applied to some lung CT images in the performed experiment. The results are shown with discussion on future work.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.15-24
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• 2013

Purpose: With the demand of SPECT/CT increasing, the interest in complex diagnostic information of CT is rising along with the expansion of various studies on potential performance value. But the study on reduction of exposure dose generated by CT is not being conducted enough. Therefore, in this study, the goal is to identify how much dose reduction exists when performing the extremity bone SPECT/CT using the flat-panel CT. Materials and Methods: The extremity bone SPECT/CT was performed with two equipments -BrightView XCT (Philips Healthcare, Cleveland, USA) and Brilliance 16 CT (Philips Healthcare, Cleveland, USA)-to identify the exposed dose and image quality resulted by changing scan parameter (mAs) applying for both equipment respectively. The noise value of image and spatial resolution were measured with AAPM CT phantom. Tube voltage (kVp) was fixed to 120 kVp, tube current (mAs) calculated at different mA (20, 30, 40, 50, 60, 70, 80) was applied to both equipments respectively. DLP (dose length product) were calculated at the same distance at respective mAs. Also, we acquired images and % contrast with NEMA IEC body phantom to confirm the effect on image. The output of statistics was analyzed by SPSS ver.18. Results: Regarding AAPM phantom, the noise decreased as the tube current (mAs) increased and flat-panel had less noise than Helical CT. This difference increased at lower dose exposure. As to the evaluation of spatial resolution, we can differentiate the space up to 0.75 mm with both equipments. With scan parameter (mA) growing, the value of DLP increased up to 54-216 mGy cm at flat-panel CT and up to 177-709 mGy cm at Helical CT. Regarding NEMA IEC body phantom, same sphere with varied parameter (mA) shows that similar results. Conclusion: There is no significant differences of image quality in both flat-panel and Helical CT when the scan parameter (mA) is changed respectively. Moreover, we can identify the reduction of exposure dose and confirm %contrast analysis value with maintaining image quality. Therefore, at the extremity bone SPECT/CT requiring high spital resolution without the wide ROI, the flat-panel CT is considered to be more useful and it expected to result in the similar image quality with lower exposure dose compared to Helical CT. Additionally, through this study, we expect to help the reduction of the unnecessary exposure dose.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.326-327
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• 2007

We investigated whether the CT images of hepatic lesions could be analyzed by computer-aided diagnosis (CAD) tool. We retrospectively reanalyzed 14 liver CT images (10 hepatocellular cancers and 4 benign liver lesions; patients who presented with hepatic masses). The hepatic lesions on CT were segmented by rectangular ROI technique and the morphologic features were extracted and quantitated using fractal texture analysis. The contrast enhancement of hepatic lesions was also quantified and added to the differential diagnosis. The best discriminating function combining the textural features and the values of contrast enhancement of the lesions was created using linear discriminant analysis. Textural feature analysis showed moderate accuracy in the differential diagnosis of hepatic lesions, but statistically insignificant. Combining textural analysis and contrast enhancement value resulted in improved diagnostic accuracy, but further studies are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.151-156
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• 2011

The analysis and image evaluation the Ray-sum technique and Shaded Surface Display (under SSD) technique which is the reconstruction image processing technique after the CT scan was evaluated and the usability of the three-dimensional information offering was confirmed in the patient with stomach cancer. After obtaining the raw data by using 64-MDCT in 20 patient with stomach cancers, the image reconstruction processing was done. It was evaluated to describe accurately the analyzed result Ray-sum and SSD reconstruction image everyone anatomical structure. In the precision estimation of the image, the lesion location could coincide in the Ray-sum and SSD reconstruction image majority with the gastro fiberscope and we can know than the gastro fiberscope over 6cm that there was the error. In addition, We could know that degree of accordance of the results of the image interpretation about the lesion and endoscope and pathological opinion were high.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.542-557
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• 2019

The behavior of rock mass is influenced by its microscopic feature of internal structure generating from forming and metamorphic process. This study investigated a new methodology for characterization of rock based on the X-ray CT (computed tomography) images reflecting the spatial distribution characteristics of internal constituent materials. The X-ray image based analysis is capable of quantification of heterogeneity and anisotropy of rock fabric, size distribution and shape parameter analysis of rock mineral grains, fluid flow simulation based on pore geometry image and roughness evaluation of unexposed joint surface which are hardly acquired by conventional rock testing methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.143-149
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• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.81-85
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• 2017

The aim of this study was to evaluate the usability of applied Low dose Computed Tomography(LDCT) protocol in examining urinary calculus using computed tomography. The subjects of this study were urological patients who visited a medical institution located in Busan from June to December 2016 and the protocol used in this study was Adaptive Statistical Iterative Reconstruction: low-dose CT with 50% Adaptive Statistical Iterative Reconstruction (ASIR). As results of quantitative analysis, the mean pixel value and standard deviation within kidney region of image(ROI)of the axial image were $26.21{\pm}7.08$ in abdomen CT pre scan and $20.03{\pm}8.16$ in low-dose CT. Also the mean pixel value and standard deviation within kidney ROI of the coronal image were $22.07{\pm}7.35$ in abdomen CT pre scan and $21.67{\pm}6.11$ in low dose CT. The results of qualitative analysis showed that four raters' mean values of observed kidney artifacts were $19.14{\pm}0.36$ when using abdomen CT protocol and $19.17{\pm}0.43$ in low-dose CT, and the mean value of resolution and contrast was $19.35{\pm}0.70$ when using abdomen CT protocol and $19.29{\pm}0.58$ in low-dose CT. Also the results of a exposure dose analysis showed that the mean values of CTDIvol and DLP in abdomen CT pre scan were 18.02 mGy and $887.51mGy{\cdot}cm$ respectively and the mean values of CTDIvol and DLP when using low-dose CT protocol were 7.412 mGy and $361.22mGy{\cdot}cm$ respectively. The resulting dose reduction rate was 58.82% and 59.29%, respectively.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1383-1391
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• 2011

Following the appearance of the latest medical equipment with improved function, the importance of image analysis which enables effective image processing and analysis consistent with the hardware performance is on the rise. As well as, ongoing study is being done on the 2D medical image processing and 3D reconstruction. This paper segments chest CT images into each stage and finally shows 3D reconstruction of each segmented result. Among various image segmentation methods, Region Growing and apply sharpening and Gamma Controller as for image improvement for effective segmentation, image segmentation in order of bronchus and lung, bronchus, lung. Human organs image of segmented is use VTK(Visualization Toolkit) to make 3D reconstruction, two and three-dimensional medical image processing and analysis for lesions diagnosis are able to utilized.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.276-286
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• 2013

SVD-based deconvolution algorithm has been known as the most effective technique for CT perfusion image analysis. In this algorithm, in order to reduce noise effects, SVD coefficients smaller than a certain threshold are removed. As the truncation threshold, either a fixed value or a variable threshold yielding a predetermined OI (oscillation index) is frequently employed. Each of these two thresholding methods has an advantage to the other either in accuracy or efficiency. In this paper, we propose a Monte Carlo simulation method to evaluate the accuracy of the two methods. An extension of the proposed method is presented as well to measure the effects of image smoothing on the accuracy of the thresholding methods. In this paper, after the simulation method is described, experimental results are presented using both simulated data and real CT images.