• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.233-242
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• 1998

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray ultrasonic and CT images. This paper introduces a method that recognizes disk diseases from spine MR images. In this method, image enhancement, image segmentation and feature extraction for sagittal plane and axial plane images are performed to separate the disk region. And then template matching method is used to extract disease region for axial plane imges. Finally, disease feature vectors are integrated and disease discrimination processes are performed. Experimental results show that the proposed method discriminates between normal and diseased disk with a considerable recognition ratio.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.113-122
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• 1997

The purpose of this study was to examine brain structural abnormalities in autistic children. Magnetic resonance imaging(MRI) findings in 22 male children with a DSM-Ⅲ-R diagnosis of autistic disorder and 17 non-autistic male control children were investigated. The ratio measures by lineometry was used to examine the cerebrum, midbrain, cerebellum, brain stem and ventricular system. The left to right ratio of the lateral ventricle was larger in autistic children than in controls. The pons was significantly larger in autistic children than in controls, and the cerebellum was smaller in autistic children. There were no significant differences between autistic children and controls in the symmetricity of the fontral lobe, parietal lobe, occipital lobe and temporal lobe, and in the size of the midbrain and 4th ventricle. These findings suggest that autistic disorder may be related to structural impairment of the brain.

• Proceedings of the KSMRM Conference
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• 1999.04a
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• pp.34-42
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• 1999

의료영상에서 인공물(Artifacts) 이라 함은 영상이 얻어지는 신체부위와 아무런 관련이 없으나 얻어진 영상에는 마치 영상의 일부분으로 나타나는 모든 것을 가리킨다. 따라서 영상에서 이들 인공물들은 실제 조직의 해부학적인 구조를 나타내지 않으므로 영상 판독에 영향을 주어 잘못된 진단을 초래할 수도 있다. 그러나 MR 영상이 가능한 이래로 새로운 여러 종류의 MR 인공물들이 많이 발견 되었으나 다행스럽게도 거의 모든 MR 인공물들은 쉽게 설명이 가능하며, 따라서 이들 인공물들에 의한 진단 오류의 가능성은 매우 희박한 실정이다. 그러나 새로운 영상방법이나 혹은 새로운 펄스대열이 계속 고안됨에 따라 새로운 종류의 인 공물들이 생겨날 가능성은 항상 존재하고 있다. 지금까지 알려진 여러 MR 인공물들은 그 생겨난 원인에 따라 다음과 같이 크게 세 가지로 분류가 가능하다. I. Motion Artifacts 1. Voluntary motion 2. Involuntary motion 1) Bowel Peristalsis 2) Respiration 3) Cardiac and vessel pulsation 4) Swallowing 3. Fluid motion 1) Blood flow 2) Cerebrospinal fluid flow II. Reconstruction Artifacts 1. Aliasing 2. Partial volume averaging 3. Truncation (Ringing) 4. Central point III. Magnetic and RF Field Related Artifacts 1. Chemical shift 1) First kind 2) Second kind 2. Susceptibility 1) Dental 2) Metal 3. Magic angle 4. Zipper 5. Bad data point 6. RF field inhomogeneity 7. Magnetic field inhomogeneity 8. Eddy current 9. slice overlapping 10. Zebra 11. RF overflow

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.478-480
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• 2002

영상 분할은 임상에서의 진단과 분석 및 3차원 가시화를 위해 선행되어야 할 필수 과정이다. 의료영상은 영상이 가지는 데이터 자체의 고유한 제약들과 해부학적 변이성 때문에 영상분할에 어려움이 있다. 본 논문에서는 의료영상의 분할을 위해 스네이크의 새로운 외부 힘으로 Gradient Vector Flow(GVF)를 이용한 방법을 제안한다. 제안된 방법은 2차원 의료영상에서 에지 맵(edge map)을 구하고, GVF을 계산하여 스네이크의 경계선과 같이 관심 있는 특징의 에너지 함수가 최소가 되는 GVF 스네이크(snake)를 구한다. 제안된 방법을 초음파영상과 자기공명영상 같은 의료영상의 분할에 적용한 결과 기존의 스네이크와 달리 잡음이나 오목한 부분이 있는 객체들을 성공적으로 분할하였다.

• Journal of the Korean Society of Radiology
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• v.79 no.5
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• pp.254-258
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• 2018

Purpose: Establishment of an appropriate protocol for breast magnetic resonance imaging (MRI) in the study of image quality standards to enhance the effectiveness of medical image information exchange, which is part of the construction and activation of clinical information exchange for healthcare informatization. Materials and Methods: The recommended protocols of breast and MRI scans were reviewed and the questionnaire was prepared by a responsible researcher. Then, a panel of 9 breast dedicated radiologists was set up in Korea. The expert panel conducted a total of three Delphi agreements to draw up a consensus on the breast MRI protocol. Results: The agreed breast MRI recommendation protocol is a 1.5 Tesla or higher device that acquires images with prone position using a breast dedicated coil and includes T2-weighted and pre-contrast T1-weighted images. Contrast enhancement images are acquired at least two times, and include 60-120 seconds between images and after 4 minutes. The contrast enhancement T1-weighted image should be less than 3 mm in thickness, less than 120 seconds in temporal resolution, and less than $1.5mm^2$ in-plane pixel resolution. Conclusion: The Delphi agreement of the domestic breast imaging specialist group has established the recommendation protocol of the effective breast MRI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1551-1555
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• 2003

This paper describes the extraction method for cerebro-region using cellular automata from the cerebrum MR images. The cerebro-region extracted from applying the cellular automata rules obtained from histogram distribution analysis after removing the background image from the cerebro-region by determining the critical value. The experiment results showed that PSNR is 42㏈ on the image quality and the correlation factor is estimated 98%. And the result can be used as the medical auto-diagnostics system of cerebrum.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.12
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• pp.102-107
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• 2009

As technology gets developed, medical equipments are also modernized and leading-edge systems, such as PACS become popular. Many scientists noticed importance of medical image processing technology. Technique of region segmentation is the first step of digital medical image processing. Segmentation technique helps doctors to find out abnormal symptoms early, such as tumors, edema, and necrotic tissue, and helps to diagnoses correctly. Segmentation of white matter, gray matter and CSF of a brain image is very crucial part. However, the segmentation is not easy due to ambiguous boundaries and inhomogeneous physical characteristics. The rate of incorrect segmentation is high because of these difficulties. Fuzzy-based segmentation algorithms are robust to even ambiguous boundaries. In this paper a modified Fuzzy-based segmentation algorithm is proposed to handle the noise of MR scanners. A proposed algorithm requires minimal computations of mean and variance of neighbor pixels to adjust a new neighbor list. With the addition of minimal compuation, the modified FCM(mFCM) lowers the rate of incorrect clustering below 30% approximately compared the traditional FCM.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.149-154
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• 1999

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray, ultrasonic and CT images. This paper introduces a method that segments and detects lesion for 2 dimensional axial MR brain images automatically. Image segmentation process consists of 2 stages. First stage extracts cerebrum region using thresholding and morphology. In the second stage, white matter, gray matter and cerebrospinal fluid in the cerebrum are extracted using FCM, We could improve processing time as removing uninterested region. Finally symmetry measure and anatomical Knowledge are used to detect lesion.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.34 no.3
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• pp.80-91
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• 2021

Objective : The purpose of this study is to discriminate the vestibular schwannoma misdiagnosed as Idiopathic Sudden Sensorineural Hearing Loss. Methods : A 46-year-old female patient who was suffering left sudden sensorineural hearing loss(SSNHL), visited after diagnosed as Idiopathic SSNHL by previous hospital. For diagnosing the vestibular schwannoma, we conducted the Puretone audiometry, auditory brainstem response threshold test and magnetic resonance imaging(MRI) for temporal bone with enhancement. Result : Auditory Brainstem Response threshold test was abnormal and in enhanced MRI, the vestibular schwannoma in left side was detected. The patient was discharged from the hospital for tertiary hospital care. Conclusions : When the patient with SSNHL visits a hospital even if after diagnosed as Idiopathic SSNHL by previous hospital, a doctor should keep in mind the possibility of vestibular schwannoma.