• Journal of Biomedical Engineering Research
• /
• v.23 no.3
• /
• pp.253-261
• /
• 2002

A spatially adaptive falter was formulated to imrove PET image qualify and the Performance of the filter was evaluated using simulation and phantom and human PET studies. In the proposed filter. if a pixel was identified as the edge Pixel, the Pixel value was Preserved. Otherwise a Pixel was replaced by the mean of the pixel values weighted by 2:7: 2. A Pixel was identified as the edge Pixel. if it satisfies the following conditions : the number of ADs (absolute difference between center and neighborhood pixels) which is smaller than THl (($pix_max{\times}0.1/log_2(NPM)$, NPM : mean of 6 neighborhood pixels excluding minimum and maximum) is 8-k and the number of ADs which is lager than TH2 ($NPM{\times}0.1$) is k. where k : 2, 3, …, 6. The results of this study demonstrate the superior performance of the Proposed titter compared to Gaussian fitter, weight median filter and subset averaged median filter. The proposed tittering method is simple but effective in increasing uniformity and contrast with minimal degradation of spatial resolution of PET images and thus. is expected to Provide improved diagnositc quality PET images .

• The KIPS Transactions:PartB
• /
• v.18B no.6
• /
• pp.341-348
• /
• 2011

ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches' results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.

• The Journal of the Korea Contents Association
• /
• v.7 no.12
• /
• pp.346-354
• /
• 2007

Interpolation methods to get the magnified image from an image with low resolution use known pixels to make an interpolated pixel. This interpolation process usually generates blurred edges and blocking effect in the result image. To improve these defects, conventional methods multiply proper weights reflecting neighborhood pixels and add the values during interpolating process. The proposed method changes input pixels in consideration of information of neighborhood pixels, gets interpolated pixels by using these values and improves the quality of interpolated image. Firstly, we compute difference values of the diagonal directions of a pixel and classify flat regions and complex regions according to these values. If the regions is complex ones, the proposed method changes an original pixel into a new value using a input pixel and a median value of it's neighbor pixels. Therefore, the proposed method applies bilinear method to the original pixels in flat regions and the changed ones in complex regions and produces the interpolated images. We evaluate the performance of the proposed method with existing methods by using PSNR and the quality of enlarged image. The results show that the proposed method improves PSNR in comparing with conventional methods and that is superior to the existing methods in terms of the quality of the interpolated image.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.39 no.11
• /
• pp.24-32
• /
• 2002

In this paper we implemented various image processing filtering using the format converter. This design method is based on realized the large processor-per-pixel array by integrated circuit technology. These two types of integrated structure are can be classify associative parallel processor and parallel process DRAM(or SRAM) cell. Layout pitch of one-bit-wide logic is identical memory cell pitch to array high density PEs in integrate structure. This format converter design has control path implementation efficiently, and can be utilize the high technology without complicated controller hardware. Sequence of array instruction are generated by host computer before process start, and instructions are saved on unit controller. Host computer is executed the pixel-parallel operation starting at saved instructions after processing start. As a result, we obtained three result that 1)simple smoothing suppresses higher spatial frequencies, reducing noise but also blurring edges, 2) a smoothing and segmentation process reduces noise while preserving sharp edges, and 3) median filtering, like smoothing and segmentation, may be applied to reduce image noise. Median filtering eliminates spikes while maintaining sharp edges and preserving monotonic variations in pixel values.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.10 no.16
• /
• pp.101-106
• /
• 1987

Statistical control charts are useful tools to monitor and control the manufacturing processes and are widely used in most Korean industries. Many Korean companies, however, do not always obtain desired results from the traditional control charts by Shewhart such as the $\bar{X}$-chart, $\bar{X}$-chart, $\bar{X}$-chart, etc. This is partly because the quality charterstics of the process are not distributed normally but are skewed due to the intermittent production, small lot size, etc. In Shewhart $\bar{X}$-chart. which is the most widely used one in Kora, such skewed distributions make the plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such shortcomings in nonnormally distributed processes, a distribution-free type of confidence interval can be used, which should be based on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for nonnormal distributions may be easily analyzed. Control limits and central lines are given for the more famous nonnormal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, Truncated-normal distributions. Robustness of the proposed median control chart is compared with that of the $\bar{X}$-chart; the former tends to be superior to the latter as the probability distribution of the process becomes more skewed. The average run length to detect the assignable cause is also compared when the process has a Normal or a Gamma distribution for which the properties of X are easy to verify, the proposed chart is slightly worse than the $\bar{X}$-chart for the normally distributed product but much better for Gamma-distributed products. Average Run Lengths of the other distributions are also computed. To use the proposed control chart, the probability distribution of the process should be known or estimated. If it is not possible, the results of comparison of the robustness force us to use the proposed median control chart based oh a normal distribution. To estimate the distribution of the process, Sturge's formula is used to graph the histogram and the method of probability plotting, $\chi$$^2$-goodness of fit test and Kolmogorov-Smirnov test, are discussed with real case examples. A comparison of the proposed median chart and the $\bar{X}$ chart was also performed with these examples and the median chart turned out to be superior to the $\bar{X}$-chart.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.4
• /
• pp.125-132
• /
• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.