• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1889-1891
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• 2004

The differential white blood cell count plays an important role in the diagnosis of different diseases. It is a tedious task to count these classes of cell manually. An automatic counter using computer vision helps to perform this medical test rapidly and accurately. Most commercial-available automatic white blood cell analysis composed mainly 3 steps including segmentation, feature extraction and classification. In this paper we concentrate on the first step in automatic white-blood-cell analysis by proposing a segmentation scheme that utilizes a benefit of active contour. Specifically, the binary image is obtained by thresolding of the input blood smear image. The initial shape of active is then placed roughly inside the white blood cell and allowed to grow to fit the shape of individual white blood cell. The white blood cell is then separated using the extracted contour. The force that drives the active contour is the combination of gradient vector flow force and balloon force. Our purposed technique can handle very promising to separate the remaining red blood cells.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.317-322
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• 1989

In this paper, brain CT images are automatically segmented to reconstruct the 3-D scene from consecutive CT sections. Contextual segmentation technique was applied to overcome the partial volume artifact and statistical fluctuation phenomenon of soft tissue images. Images are hierarchically analyzed by region growing and graph editing techniques. Segmented regions are discriptively decided to the final organs by using the semantic informations.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.33-37
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• 1990

In this paper, a new method for lung area segmentation in chest radiographs has been presented. The movivation of this study is to include fuzzy informations about the relation between the image date structure and the area to be segmented in the segmentation process efficiently. The proposed method approached the segmentation problem in the perspective of pattern classification, using trainable pattern classifier, multi-layer perceptron. Having been trained with 10 samples, this method gives acceptable segmentation results, and also demonstrated the desirable property of giving better results as the training continues with more training samples.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.191-197
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• 2013

Conventional CT and MRI scans produce cross-section slices of body that are viewed sequentially by radiologists who must imagine or extrapolate from these views what the 3 dimensional anatomy should be. By using sophisticated algorithm and high performance computing, these cross-sections may be rendered as direct 3D representations of human anatomy. The 2D medical image analysis forced to use time-consuming, subjective, error-prone manual techniques, such as slice tracing and region painting, for extracting regions of interest. To overcome the drawbacks of 2D medical image analysis, combining with medical image processing, 3D visualization is essential for extracting anatomical structures and making measurements. We used the gray-level thresholding, region growing, contour following, deformable model to segment human organ and used the feature vectors from texture analysis to detect harmful cancer. We used the perspective projection and marching cube algorithm to render the surface from volumetric MR and CT image data. The 3D visualization of human anatomy and segmented human organ provides valuable benefits for radiation treatment planning, surgical planning, surgery simulation, image guided surgery and interventional imaging applications.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.39-42
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• 2000

In our study, by observing and analyzing normal liver in abdominal CT image, we estimated gray value range and generated binary image. In the binary image, we achieved the number of hole which is located between pixels. Depending on the ratio, we processed the input image to 4 kinds of mesh images to remove the noise part that has the different ratio. With the Union image of 4 kinds of mesh images, we generated the template representing general outline of liver and subtracted from the binary image so the we can represent the organ boundary to be minute. With results of proposed method, processing time is reduced compared with existing method and we compared the result image to manual image of medical specialists.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.71-74
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• 1990

In the medical modalities, three-dimensional objects must be reconstructed from the consecutive slices. but the slime separation is usually much greater than the pixel size within an individual slices. In this paper, an interpolation scheme for filling the spare between the shapes in two successive slices is developed. It minimizes the computation involvement in segmentation of 3-D reconst ructlon process as well as more accurately approximates the object than the linear interpolation method.

• Smart Media Journal
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• v.5 no.1
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• pp.49-54
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• 2016

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.100-106
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• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1765-1772
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• 2012

Success rate of transplantation of body organs improved due to development of medical equipment and diagnostic technology. In particular, a liver transplant due to liver dysfunction has increased. With the development of image processing and analysis to obtain the volume for liver transplantation have increased the accuracy and efficiency. In this thesis, we try to reconstruct the regions of the liver within three dimensional images using the mevislab tool, which is effective in quick comparison and analysis of various algorithms, and in expedient development of prototypes. Liver is divided by applying threshold values and region growing method to the original image, and by removing noise and unnecessary entities through morphology and region filling, and setting of areas of interest. It is deemed that high temporal efficiency, and presentation of diverse range of comparison and analysis module application methods through usage of MeVisLab would make contribution towards expanding of baseline of medical image processing researches.