• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.189-194
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• 2004

Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of a pancreas in the abdomen. In this paper, an automatic liver segmentation method using a partial histogram threshold (PHT) algorithm is proposed for overcoming randomness of CE-CT images and removing the pancreas. After histogram transformation, adaptive multi-modal threshold is used to find the range of gray-level values of the liver structure. Also, the PHT algorithm is performed for removing the pancreas. Then, morphological filtering is processed for removing of unnecessary objects and smoothing of the boundary. Four CE-CT slices of eight patients were selected to evaluate the proposed method. As the average of normalized average area of the automatic segmented method II (ASM II) using the PHT and manual segmented method (MSM) are 0.1671 and 0.1711, these two method shows very small differences. Also, the average area error rate between the ASM II and MSM is 6.8339 ％. From the results of experiments, the proposed method has similar performance as the MSM by medical Doctor.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.65-75
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• 2006

This thesis deals with a wavelet-based method of denoising of infrared images contaminated with impulse noise and Gaussian noise, he method of thresholding the wavelet coefficients using derivatives and median absolute deviations of the wavelet coefficients of the detail subbands was proposed to effectively denoise infrared images with noises. Particularly, in order to eliminate the impulse noise the method of generating binary masks indicating locations of the impulse noise was selected. By this method, the threshold values dividing edges and noises were obtained more effectively proving the validity of the denoising method compared with the conventional wavelet shrinkage method.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.39-47
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• 2007

In this thesis, a method of generating an adaptable Region-Of-Interest(ROI) Mask for the Region-Of-Interest coding is suggested. In the method, an ROI Mask is generated using the information of the ROI designated by a user. In the existed method of ROI coding, after scanning all the pixels in order and discriminating an ROI, an ROI Mask is generated. But, in our method, after scanning a part of pixels based on the shape pattern of an ROI and discriminating a ROI by one code block unit, an ROI Mask is generated. Moreover, from the method, a pattern number, threshold of a ROI and background threshold parameter are provided. According to the result of its comparing test with the existed methods to show the usability, it is proved that our method is superior in speed to the existed ones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.166-174
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. The important problem in recording ECG signal is a baseline wandering, which is occurred by rhythm of respiration and muscle contraction attaching to an electrode. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, baseline wander removing method based on morphological filter for efficient QRS detection method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. The signal distortion ratio of the proposed method is compared with other filtering method. Also, R wave detection is evaluated by using MIT-BIH arrhythmia database. Experiment result show that proposed method removes baseline wanders effectively without significant morphological distortion.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2775-2778
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• 2003

Neural network-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.223-230
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• 2001

This paper proposes a new contour detection method and adaptive reconstruction scheme for the cylindrical organs, such as blood vessels or arteries. Furthermore, we present java-based navigation controller which has been built to examine the inside of cylindrical objects. Tn the preprocessing procedure, a few preprocessing image filters are applied in order to remove unwanted artifacts from the medical images and to estimate threshold values for the object of interest. We define a context-free grammar, which is proper fur properties of contours of cylindrical objects. In the next procedure, we extract contours using advanced radial gradient method and represent contours as context-free grammar derivation trees. We build polygons between two contours efficiently by traversing the derivations trees of the contours. We fly through the reconstructed virtual models using java-based navigation controller and VRML viewer.

• Journal of Biomedical Engineering Research
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• v.29 no.5
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• pp.348-354
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• 2008

Segmenting vessels in lower extremity CT images is very difficult because of gray level variation, connection to bones, and their small sizes. Instead of segmenting vessels, we propose an approach that segments bones and subtracts them from the original CT images. The subtracted images can contain not only connected vessel structures but also isolated vessels, which are very difficult to detect using conventional vessel segmentation methods. The proposed method initially grows a 3-dimensional (3D) volume with a seeded region growing (SRG) using an adaptive threshold and then detects junctions and forked branches. The forked branches are classified into either bone branches or vessel branches based on appearance, shape, size change, and moving velocity of the branch. The final volume is re-grown by collecting connected bone branches. The algorithm has produced promising results for segmenting bone structures in several tens of vessel-enhanced CT image data sets of lower extremities.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.866-875
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• 1991

In this paper, multi pulse excited-linear predictive coding (MPE-LPC), where the correlation eliminated residual signal is modeled by a few pules, is shown to be effective for the compression of electrocardiogram (ECG) data, and a more efficient scheme for a faithful reconstruction of ECG is proposed. The reconstruction charateristic of QRS's and P.T waves is improved using the adaptive pulse allocation (APA), and the compression ratio (CR) can be changed by controlling the mumber of modeling pulses. The performance of the proposed method was evaluated using 10 normal and 10 abnormal ECG data. The proposed method had a better performance than the variable threshold amplitude zone time epoch coding (AZTEC) algorithm and the scan-along polygonal approximation (SAPA) algorithm with the same CR. With the CR in kthe range of 8:1 to 14:1, we could compress ECG data efficiently.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.53-59
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• 2011

This paper proposes a new method of traffic signal detection and recognition in an RGB color model. The proposed method firstly processes RGB-filtering in order to detect traffic signal candidates. Secondly, it performs adaptive threshold processing and then analyzes connected components of the binary image. The connected component of a traffic signal has to be satisfied with both a bounding box rate and an area rate that are defined in this paper. The traffic signal recognition system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.333-336
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• 2002

This paper presents a new image segmentation method using fuzzy measures which reflect the local property of an image as well as the global property of an image An image is globally segmented into the crisp region and the ambiguous region in terms of the Index of fuzziness measured over all pixels of an image. The ambiguous region is luther partitioned into background and object in terms of the index of fuzziness computed over the set of neighboring pixels reflecting the local property most. From the experimental results, this method shows the effective ambiguity handling capability in segmenting an image.