• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.61-68
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• 2003

This paper presents a quantitative analysis method for fall in sick times of the cerebral infarction patients using three types of magnetic resonance image, which play an important role in deciding method of medical treatment. For this object, image characteristics obtained by three radiographic methods of MRI and their relation were analyzed by means of object centered hierarchical Planning method. This methode presents an approach to the knowledge based processes for image interpretation and analysis. To compare three type of MRI. a multiple warping algorithm and affine transform method performed for image matching. Then each fall in sick times level of cerebral infarction was quantified and pseudo-color mapping performed by comparing gray level value one another according to Previously obtained hand maid data. The result of this study was compared to a medical doctors decision.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.409-417
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• 2006

Research on neural circuit is a difficult area due to complexity and inaccessibility. Due to recent developments, the research using multi-electrode array of cells or tissues has become an important research area. However, there are some difficulties to decode the submerged meaning from huge and complex neural data. Moreover, it needs a harmonic collaboration between informatics and bioscience. In this paper, we have developed a custom-designed signal processing technique for multi-electrode array measured neural responses induced by electrical stimuli to the hippocampal tissue slices of the rat brain. The raw data from hippocampal slice using the multi-electrode array system were saved in a computer. Then we estimated characteristic points in each channel and calculated the total activity. To estimate the points, we used the Polynomial Fitting Approximation Method. Using the calculated total activity, we could provide the histogram or pseudo-image matrix to help interpretation of results.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.589-596
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• 2019

X-rays used for diagnosis have a continuous energy distribution. However, photons with low energy not only reduce image contrast, but also contribute to the patient's radiation exposure. Therefore, clinics currently use filters made of aluminum. Such filters are advantageous because they can reduce the exposure of the patient to radiation. However, they may have negative effects on imaging quality, as they lead to increases in the scattered dose. In this study, we investigated the effects of the scattered dose generated by an aluminum filter on medical image quality. We used the relative standard deviation and the scatter degradation factor as evaluation indices, as they can be used to quantitatively express the decrease in the degree of contrast in imaging. We verified that the scattered dose generated by the increase in the thickness of the aluminum filter causes degradation of the quality of medical images.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.2
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• pp.137-142
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• 2009

Purpose: This study was performed to find the current problems of positron emission tomography/computed tomography(PET/CT) data on CD for inter-hospital transfer. Materials and Methods: The subjects were 746 consecutive $^{18}F$-fluorodeoxyglucose PET/CT data CDs from 56 hospitals referred to our department for image interpretation. The formats and contents of PET/CT data CDs were reviewed and the email questionnaire survey about this was performed. Results: PET/CT data CDs from 21 of 56 hospitals(37.5%) included all transaxial CT and PET images with DICOM standard format which were required for authentic interpretation. PET/CT data from the others included only secondary capture images or fusion PET/CT images. According to this survey, the main reason of limited PET/CT data on CD for inter-hospital transfer was that the data volume of PET/CT was too large to upload to the Picture Archiving and Communication System. Conclusion: The majority of hospitals provided limited PET/CT data on CD for inter-hospital transfer, which could be inadequate for accurate interpretation and clinical decision making. It is necessary to standardize the format of PET/CT data on CD for inter-hospital transfer including all transaxial CT and PET images with DICOM standard format.

• Annals of Clinical Neurophysiology
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• v.25 no.2
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• pp.55-65
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• 2023

The ultimate role of ocular movements is to keep the image of an object within the fovea and thereby prevent image slippage on the retina. Accurate evaluations of eye movements provide very useful information for understanding the functions of the oculomotor system and determining abnormalities therein. Such evaluations also play an important role in enabling accurate diagnoses by identifying the location of lesions and discriminating from other diseases. There are various types of ocular movements, and this article focuses on saccades, fast eye movements, smooth pursuit, and slow eye movements, which are the most important types of eye movements used in evaluations performed in clinical practice.

• Korean Journal of Radiology
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• v.23 no.2
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• pp.246-255
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• 2022

Objective: To determine the usefulness of Silent MR angiography (MRA) for evaluating intracranial aneurysms treated with stent-assisted coil embolization. Materials and Methods: Ninety-nine patients (101 aneurysms) treated with stent-assisted coil embolization (Neuroform atlas, 71 cases; Enterprise, 17; LVIS Jr, 9; and Solitaire AB, 4 cases) underwent time-of-flight (TOF) MRA and Silent MRA in the same session using a 3T MRI system within 24 hours of embolization. Two radiologists independently interpreted both MRA images retrospectively and rated the image quality using a 5-point Likert scale. The image quality and diagnostic accuracy of the two modalities in the detection of aneurysm occlusion were further compared based on the stent design and the site of aneurysm. Results: The average image quality scores of the Silent MRA and TOF MRA were 4.38 ± 0.83 and 2.78 ± 1.04, respectively (p < 0.001), with an almost perfect interobserver agreement. Silent MRA had a significantly higher image quality score than TOF MRA at the distal internal carotid artery (n = 57, 4.25 ± 0.91 vs. 3.05 ± 1.16, p < 0.001), middle cerebral artery (n = 21, 4.57 ± 0.75 vs. 2.19 ± 0.68, p < 0.001), anterior cerebral artery (n = 13, 4.54 ± 0.66 vs. 2.46 ± 0.66, p < 0.001), and posterior circulation artery (n = 10, 4.50 ± 0.71 vs. 2.90 ± 0.74, p = 0.013). Silent MRA had superior image quality score to TOF MRA in the stented arteries when using Neuroform atlas (4.66 ± 0.53 vs. 3.21 ± 0.84, p < 0.001), Enterprise (3.29 ± 1.59 vs. 1.59 ± 0.51, p = 0.003), LVIS Jr (4.33 ± 1.89 vs. 1.89 ± 0.78, p = 0.033), and Solitaire AB stents (4.00 ± 2.25 vs. 2.25 ± 0.96, p = 0.356). The interpretation of the status of aneurysm occlusion exhibited significantly higher sensitivity with Silent MRA than with TOF MRA when using the Neuroform Atlas stent (96.4% vs. 14.3%, respectively, p < 0.001) and LVIS Jr stent (100% vs. 20%, respectively, p = 0.046). Conclusion: Silent MRA can be useful to evaluate aneurysms treated with stent-assisted coil embolization, regardless of the aneurysm location and type of stent used.

• Journal of the Korean Society of Radiology
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• v.8 no.1
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• pp.27-33
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• 2014

Medical imaging has increased rapidly in the increase of interest in health, with the development of computer technology, digitization of medical imaging is rapidly advancing, PACS has been introduced to the medical field. Increase in the production of medical images by these phenomena made increased the workload of radiologist who must read a medical image. in response to the need for secondary diagnosis using a computer, The term of CAD in medical radiology field was introduced. In this study, we have proposed a CAD algorithm for the interpretation of the image obtained by the digital X-ray mammography equipment. The experiments were performed by programmed in Visual C++ for the proposed algorithm. A result of the execution of the CAD algorithm seven sample images, the results of five samples was confirmed in breast cancer and benign tumors, both the images sample was error processing. If you use a program that implements this with the algorithm proposed in this study it is helpful to reading breast images, and it is considered to contribute significantly to the early detection of breast cancer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.255-258
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• 2008

For rapidly increasing amount of medical images, it is difficult for radiologist to interpretate the medical images fastly and for sufficient time. We investigated whether liver CT image has good features to be analyzed by computer algorithm, We examed the CT images of liver tumors (Hepetocellular carcinomas; HCCs) and searched any potential morphologic characteristics to be analyzed by computer algorithms. On unenhanced CT, HCCs appeared hypodense After enhancement, most HCCs were hyperdense, and then. as a consequence of rapid washout, HCCS became hypodense compared with the liver parenchyma. Most CT images of HCCs showed synchronous phase-specific.morphologic features. We applied various edge detection filters to these images and some filters showed favorable performance in the detection of tile edge of liver and HCC. Therefore, theses features seems to be analyzed by computer algorithms effectively.Further studies may be warranted.

• Journal of radiological science and technology
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• v.32 no.2
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• pp.147-151
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• 2009

In general, the discrete confidence judgments that use five-step assessment method have been used to assess the medical images by ROC. TPF or FPF can be computed easily with this independent reading test. However, during experiments, it happens frequently that adequate distribution for observers is required to smoothly estimate the ROC curve. In addition, data becomes invalid for distribution of the created categories. To solve such problems or to apply the ROC interpretation to data that is not obtained from the experimental observation, the continuous confidence judgements (CCJ) has been proposed, which implements ROC interpretation using continuously-distributed experimental results without category classification has been used. As the use of CCJ to assess medical images was barely reported in Korea, we applied it to the assessment of chest digital images in this study. The results showed that a smooth ROC curve was obtained conveniently by the commercialized program and the characteristic value was measured easily. Therefore, it is recommended that this method can be applied to the assessment of digital medical images.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.