• Progress in Medical Physics
• /
• v.10 no.1
• /
• pp.33-40
• /
• 1999

Purpose: To simulate and measure the signal intensity of various tissues near bone interface in 2D and 3D neurological MR images. Materials and Methods: In neurological proton density (PD) weighted images, every component in the head including cerebrospinal fluid (CSF), muscle and scalp, with the exception of bone, are visualised. It is possible to acquire images in 2D or 3D. A 2D fast spin-echo (FSE) sequence is chosen for the 2D acquisition and a 3D gradient-echo (GE) sequence is chosen for the 3D acquisition. To find out the signal intensities of CSF, muscle and fat (or scalp) for the 2D spin-echo(SE) and 3D gradient-echo (GE) imaging sequences, the theoretical signal intensities for 2D SE and 3D GE were calculated. For the 2D fast spin-echo (FSE) sequence, to produce the PD weighted image, long TR (4000 ms) and short TE$_{eff}$ (22 ms) were employed. For the 3D GE sequence, low flip angle (8$^{\circ}$) with short TR (35 ms) and short TE (3 ms) was used to produce the PD weighted contrast. Results: The 2D FSE sequence has CSF, muscle and scalp with superior image contrast and SNR of 39 - 57 while the 3D GE sequence has CSF, muscle and scalp with broadly similar image contrast and SNR of 26 - 33. SNR in the FSE image were better than those in the GE image and the skull edges appeared very clearly in the FSE image due to the edge enhancement effect in the FSE sequence. Furthermore, the contrast between CSF, muscle and scalp in the 2D FSE image was significantly better than in the 3D GE image, due to the strong signal intensities (or SNR) from CSF, muscle and scalp and enhanced edges of CSF. Conclusion: The signal intensity of various tissues near bone interface in neurological MR images has been simulated and measured. Both the simulation and imaging of the 2D SE and 3D GE sequences have CSF, fat and muscle with broadly similar image intensity and SNR's and have succeeded in getting all tissues about the same signal. However, in the 2D FSE sequence, image contrast between CSF, muscle and scalp was good and SNR was relatively high, imaging time was relatively short.

• Journal of radiological science and technology
• /
• v.38 no.4
• /
• pp.451-461
• /
• 2015

In megavoltage (MV) radiotherapy, delivering the dose to the target volume is important while protecting the surrounding normal tissue. The purpose of this study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) using an edge block in megavoltage X-ray imaging (MVI). We used an edge block, which consists of tungsten with dimensions of 19 (thickness) ${\times}$ 10 (length) ${\times}$ 1 (width) $cm^3$ and measured the pre-sampling MTF at 6 MV energy. Various radiation therapy (RT) devices such as TrueBeam$^{TM}$ (Varian), BEAMVIEW$^{PLUS}$ (Siemens), iViewGT (Elekta) and Clinac$^{(R)}$iX (Varian) were used. As for MTF results, TrueBeam$^{TM}$(Varian) flattening filter free(FFF) showed the highest values of $0.46mm^{-1}$ and $1.40mm^{-1}$ for MTF 0.5 and 0.1. In NPS, iViewGT (Elekta) showed the lowest noise distribution. In DQE, iViewGT (Elekta) showed the best efficiency at a peak DQE and $1mm^{-1}DQE$ of 0.0026 and 0.00014, respectively. This study could be used not only for traditional QA imaging but also for quantitative MTF, NPS, and DQE measurement for development of an electronic portal imaging device (EPID).

• Journal of the Korea Computer Graphics Society
• /
• v.22 no.3
• /
• pp.21-29
• /
• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.44 no.11
• /
• pp.101-108
• /
• 2007

In this paper, we propose and verify an adaptive block watermarking algorithm based on JPEG2000 DWT, which determines watermarking for the original image by two scaling factors in order to overcome image degradation and blocking problem at the edge. Adaptive block watermarking algorithm uses 2 scaling factors, one is calculated by the ratio of present block average to the next block average, and the other is calculated by the ratio of total LL subband average to each block average. Signals of adaptive block watermark are obtained from an original image by itself and the strength of watermark is automatically controlled by image characters. Instead of conventional methods using identical intensity of a watermark, the proposed method uses adaptive watermark with different intensity controlled by each block. Thus, an adaptive block watermark improves the visuality of images by 4$\sim$14dB and it is robust against attacks such as filtering, JPEG2000 compression, resizing and cropping. Also we implemented the algorithm in ASIC using Hynix 0.25${\mu}m$ CMOS technology to integrate it in JPEG2000 codec chip.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.5
• /
• pp.505-513
• /
• 2014

The RapidEye can acquire the 6.5m spatial resolution satellite imagery with the high temporal resolution on each day, based on its constellation of five satellites. The image products are available in two processing levels of Basic 1B and Ortho 3A. The Basic 1B image have radiometric and sensor corrections and include RPCs (Rational Polynomial Coefficients) data. In Korea, the geometric accuracy of RapidEye imagery can be improved, based on the scaled national digital maps that had been built. In this paper, we present the fully automated procedures to georegister the 1B data using 1:25,000 digital maps. Those layers of map are selected if the layers appear well in the RapidEye image, and then the selected layers are RPCs-projected into the RapidEye 1B space for generating vector images. The automated edge-based matching between the vector image and RapidEye improves the accuracy of RPCs. The experimental results showed the accuracy improvement from 2.8 to 0.8 pixels in RMSE when compared to the maps.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.1
• /
• pp.103-119
• /
• 2010

In this paper, we propose a subpixel shift estimation method using phase correlation with a local region for the registration of noisy images. Phase correlation is commonly used to estimate the subpixel shift between images, which is derived from analyzing shifted and downsampled images. However, when the images are affected by additive white Gaussian noise and aliasing artifacts, the estimation error is increased. Thus, instead of using the whole image, the proposed method uses a specific local region that is less affect by noises. In addition, to improve the estimation accuracy, iterative phase correlation is applied between selected local regions rather than using a fitting function. the restricted range is determined by analyzing the maximum peak and the two adjacent values of the inverse Fourier transform of the normalized cross power spectrum. In the experiments, the proposed method shows higher accuracy in registering noisy images than the other methods. Thus, the edge-sharpness and clearness in the super-resolved image is also improved.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.2
• /
• pp.155-160
• /
• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Journal of the Korean association of regional geographers
• /
• v.2 no.2
• /
• pp.183-196
• /
• 1996

The purpose of this paper is to explore the possibility of automatic extraction of line feature from Satellite image. The first part reviews the relationship between spatial filtering and cartographic interpretation. The second part describes the principal operations of high frequency filters and their properties, the third part presents the result of filtering application to the SPOT Panchromatic image of the Chinju city. Some experimental results are given here indicating the high feasibility of the filtering technique. The results of the paper is summarized as follows: Firstly the good all-purposes filter dose not exist. Certain laplacian filter and Frei-chen filter were very sensitive to the noise and could not detect line features in our case. Secondly, summary filters and some other filters do an excellent job of identifying edges around urban objects. With the filtered image added to the original image, the interpretation is more easy. Thirdly, Compass gradient masks may be used to perform two-dimensional, discrete differentiation directional edge enhancement, however, in our case, the line featuring was not satisfactory. In general, the wide masks detect the broad edges and narrow masks are used to detect the sharper discontinuities. But, in our case, the difference between the $3{\times}3$ and $7{\times}7$ kernel filters are not remarkable. It may be due to the good spatial resolution of Spot scene. The filtering effect depends on local circumstance. Band or kernel size selection must be also considered. For the skillful geographical interpretation, we need to take account the more subtle qualitative information.

• Journal of the Korea Convergence Society
• /
• v.9 no.11
• /
• pp.61-68
• /
• 2018

In this paper, we propose a method of character detection by analyzing image enhancement and character type to detect characters in natural images that can be acquired in everyday life. The proposed method emphasizes the boundaries of the object part using the unsharp mask in order to improve the detection rate of the area to be recognized as a character in a natural image. By using the boundary of the enhanced object, the character candidate region of the image is detected using Maximal Stable Extermal Regions (MSER). In order to detect the region to be judged as a real character in the detected character candidate region, the shape of each region is analyzed and the non-character region other than the region having the character characteristic is removed to increase the detection rate of the actual character region. In order to compare the objective test of this paper, we compare the detection rate and the accuracy of the character region with the existing methods. Experimental results show that the proposed method improves the detection rate and accuracy of the character region over the existing character detection method.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.11
• /
• pp.981-986
• /
• 2014

Printed electronics devices are made of several sets of printed patterns. The quality or printability of the printed patterns determines the electrical performance of such devices. Moreover, control of the printability determines the reliability of such devices. Despite its importance, few studies have been reported for the measurement of the printed patterns to evaluate their printability. In this study, a measurement method is proposed for printed patterns, including the definition of the properties to be measured, and the related software is described. The proposed method measures the width, pinholes, and edge waviness and evaluates the printability of the patterns quantitatively. The proposed measurement method could be an efficient tool to evaluate and enhance the printability of printed patterns in printed electronics.