• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.4
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• pp.43-54
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• 2007

This paper presents design and verification of a face detection hardware for real time application. Face detection algorithm detects rough face position based on already acquired feature parameter data. The hardware is composed of five main modules: Integral Image Calculator, Feature Coordinate Calculator, Feature Difference Calculator, Cascade Calculator, and Window Detection. It also includes on-chip Integral Image memory and Feature Parameter memory. The face detection hardware was verified by using S3C2440A CPU of Samsung Electronics, Virtex4LX100 FPGA of Xilinx, and a CCD Camera module. Our design uses 3,251 LUTs of Xilinx FPGA and takes about 1.96${\sim}$0.13 sec for face detection depending on sliding-window step size, when synthesized for Virtex4LX100 FPGA. When synthesized on Magnachip 0.25um ASIC library, it uses about 410,000 gates (Combinational area about 345,000 gates, Noncombinational area about 65,000 gates) and takes less than 0.5 sec for face realtime detection. This size and performance shows that it is adequate to use for embedded system applications. It has been fabricated as a real chip as a part of XF1201 chip and proven to work.

• Journal of radiological science and technology
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• v.33 no.4
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• pp.341-348
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• 2010

By accessing the current status of FPD system use in the hospitals located in Seoul and Gyeonggi Province as well as the entrance skin dose and the image quality evaluation realized by C-D Phantom, and the image assessment by the medical professionals regarding the radiography for the extremity, the following results were derived. 1. According to the evaluation made in the actual use of FPD system (12 machines), the grid ratio varied from 8:1 to 13:1, and 6 machines used the grid ratio with 12:1, realizing the largest number. Among the machines, there were 8 machines that allowed a removable grid while 3 machines did use a removable grid (25.0%). 2. When it came to the equipments used for the experiment, it showed that the amount of the entrance skin dose increased from 4.13 times up to 4.79 times with the grid use. 3. The difference in the entrance skin dose depending on the changes in the exposure condition(0.5times or 2.0times) was not significantly different regardless of the patients' thickness. 4. In terms of the image quality depending on C-D Phantom, the grid use was distinguished well. However, the images were well distinguishable as the exposure condition got increased. 5. In the clinical assessment, the grid use was less effective for the Hand PA, which was considered to shoot a thin body part. It was evaluated that the grid use was preferred for the Knee AP, which was shooting for a relatively thick body part. Nonetheless, 3 out of 5 people said that they would not use the grid if the entrance skin dose to reduced.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.639-646
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• 2017

The purpose of this study is to present the algorithm to minimize the image noise caused by deterioration of high X-ray container inspection equipment and the faulty detection sensors, and to improvement quality of the container inspection images using MATLAB Toolbox. The daily checking images for the container inspection were used with the subject images and the noise caused by the horizontal and vertical images was evaluated with Root Mean Square (RMS) method, which is the most basic evaluation method of digital radiation image. Also, quality of the improved images was evaluated compared to quality of the orignal images. As a result, all RMS value of the improved images was lower then the original images by a mean of 13.5% in the horizontal images and 18.2% in the vertical images respectively. Also so did RMS value of the improved container images, by a mean of 13.4% in the horizontal images and 19.1% in the vertical images respectively. These findings can be verified objectively and visually and they would help the reading process of the container images be effective in Korea Customs Service.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1235-1242
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• 2008

In this paper, x-ray image sensor of photon counting type having a $32{\times}32$ pixel array is designed with $0.18{\mu}m$ triple-well CMOS process. Each pixel of the designed image sensor has an area of loot $100{\times}100\;{\mu}m2$ and is composed of about 400 transistors. It has an open pad of an area of $50{\times}50{\mu}m2$ of CSA(charge Sensitive Amplifier) with x-ray detector through a bump bonding. To reduce layout size, self-biased folded cascode CMOS OP amp is used instead of folded cascode OP amp with voltage bias circuit at each single-pixel CSA, and 15-bit LFSR(Linear Feedback Shift Register) counter clock generator is proposed to remove short pulse which occurs from the clock before and after it enters the counting mode. And it is designed that sensor data can be read out of the sensor column by column using a column address decoder to reduce the maximum current of the CMOS x-ray image sensor in the readout mode.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.457-464
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• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.1-9
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• 2023

For optimal image quality of computer tomography angiography (CTA), different iodine concentrations and scan parameters were applied to quantitatively evaluate the image quality characteristics of filtered back projection (FBP), hybrid-iterative reconstruction (hybrid-IR), and deep learning reconstruction (DLR). A 320-row-detector CT scanner scanned a phantom with various iodine concentrations (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4, and 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm. Data obtained using each reconstruction technique was analyzed through noise, coefficient of variation (COV), and root mean square error (RMSE). As the iodine concentration increased, the CT number value increased, but the noise change did not show any special characteristics. COV decreased with increasing iodine concentration for FBP, adaptive iterative dose reduction (AIDR) 3D, and advanced intelligent clear-IQ engine (AiCE) at various tube voltages and tube currents. In addition, when the iodine concentration was low, there was a slight difference in COV between the reconstitution techniques, but there was little difference as the iodine concentration increased. AiCE showed the characteristic that RMSE decreased as the iodine concentration increased but rather increased after a specific concentration (4.9 mg/mL). Therefore, the user will have to consider the characteristics of scan parameters such as tube current and tube voltage as well as iodine concentration according to the reconstruction technique for optimal CTA image acquisition.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.1
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• pp.46-50
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• 2018

Purpose Generally, a collimator that installed in front of detector set a direction of gamma ray and remove a scatter ray. By the way, a lateral or oblique scatter ray is detected into crystal through collimator. At this study, we will evaluate a mount of count and spectrums of lateral scatter ray. Materials and Methods We used the SKY LITE (philips, netherlands) as a gamma camera, and $^{99m}Tc$, 1.11 GBq point source as a phantom. we put this point source at backside 50 cm of detector. After acquiring this for 1 min, we turned a detector next 10 degrees. Likely this, we acquired images at every 10 degrees from $0^{\circ}$ to $360^{\circ}$, analyzed images and spectrums. In case of patient study, we choose a 3 phase bone scan patient who had a hand disease, because scatter rays from body would detect on crystal. After acquiring blood flow and blood pool images, we analyzed images and spectrums. Additional, we put a lead gown on patient's hand, body. And then we compared and evaluated 3 type blood pool images (non lead gown, lead gown on a hand and on body). Results In case of phantom study, scatter ray counts at backside ($270^{\circ}-90^{\circ}$) are same with a background count. By the way, counts of scatter ray of oblique side ($0^{\circ}-50^{\circ}$, $220^{\circ}-270^{\circ}$) are 100-600 cps, furthermore, counts at frontside are over 4 Mcps. In case of patient study, a counts of hand blood pool scan are 1510 cps. But counts of hand with lead gown on hands and on body are each 1554 cps, 1299 cps. Conclusion Therefore, even though there is a collimator in front of detector, lateral scatter rays detect on crystal and affect to images and spectrums. Especially, if there is a high activity source at outside of detector when we examine low activity organs like hands or foot, we have to shield and remove the source at outside for a good image.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.151-165
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• 2010

Images are often corrupted by impulse noise due to a noise sensor or channel transmission errors. The filter based on SVM(Support Vector Machine) and the improved adaptive median filtering is proposed to preserve image details while suppressing impulse noise for image restoration. Our approach uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a noisy pixel, the improved adaptive median filter is used to replace it. To demonstrate the performance of the proposed filter, extensive simulation experiments have been conducted under both salt-and-pepper and random-valued impulse noise models to compare our method with many other well known filters in the qualitative measure and quantitative measures such as PSNR and MAE. Experimental results indicate that the proposed filter performs significantly better than many other existing filters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.42-48
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• 2007

In x-ray imaging system, two kinds of noises are involved. First, the charge generated from the radiation interaction with the detector during exposure. Second, the signal is then added by readout electronics noise. But, x-ray images are not modeled by Gaussian noise but as the realization of a Poisson process. In this paper, we apply a new approach to remove Poisson noise from medical X-ray image in the wavelet domain, the applied methods shows more excellent results in cone-beam CT.