• Journal of KIISE:Software and Applications
• /
• v.27 no.6
• /
• pp.651-659
• /
• 2000

It is very important to extract the heart region in the medical images. In this paper, we present the automatic heart region extraction in the EBT (electron beam tomography) images. We use contrast thresholding, anatomic knowledge, and mathematical morphology to extract the heart region. Using these results, we applied the active contour models (snakes) to search the exact region. We analyzed the experimental results by comparing the results with the results made by medical experts.

• Journal of KIISE:Software and Applications
• /
• v.31 no.3
• /
• pp.276-292
• /
• 2004

In this paper. we present methods that extract lung regions from chest EBT(electron beam tomography) images then segment the extracted lung region into lung lobes. We use histogram based thresholding and mathematical morphology for extracting lung regions. For detecting pulmonary fissures, we use edge detector and knowledge-based search method. We suggest this edge detector, which uses adaptive filter scale, to work very well for real edge and insensitive for edge by noise. Our experiments showed about 95% accuracy or higher in extracting lung regions and about 5 pixel distance error in detecting pulmonary fissures.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.10b
• /
• pp.895-898
• /
• 2000

의료 영상에서 폐 영역의 정확한 추출과 폐엽의 분할은 폐 기능의 측정 및 폐 질환의 진단을 위하여 매우 중요하다. 본 논문에서는 EBT 흉부 영상에서 자동으로 폐 영역을 추출하고 폐 영역을 폐엽 단위로 분할하는 방법을 제안한다. 본 논문에서는 히스토그램 분석과 형태학적 연산자를 이용하여 폐 영역을 추출하고 adaptive filter를 이용한 에지 연산과 폐엽 경계(pulmonary fissure)에 대한 의학적 지식을 바탕으로 폐엽을 분할하였다. 본 방법을 여러 종류의 EBT 폐 영상에 적용하여 실험한 결과 95%이상의 정확도를 보였다.

• Journal of Korea Multimedia Society
• /
• v.8 no.3
• /
• pp.336-344
• /
• 2005

In this paper, we extracted the contour of lung parenchyma on EBT images with the improved active contour model. The objects boundary in conventional active contour model can be extracted by controlling internal energy and external energy as energy minimizing form. However, there are a number of problems such as initialization and the poor convergence about concave part. Expecially, contour can not enter the concave region by discouraging characteristic about stretching and bending in internal energy. We controlled internal energy by moving local perpendicular bisector point of each control point in the contour and implemented the object boundary by minimizing energy with external energy The convergence of concave part could be efficiently implemented toward lung parenchyma region by this internal energy and both lung images for initial contour could also be detected by multi-detection method. We were sure this method could be applied detection of lung parenchyma region in medical image.

• Radiation Oncology Journal
• /
• v.25 no.4
• /
• pp.249-260
• /
• 2007

Purpose: In order to enhance the quality of IMRT as employed in Korea, we developed a remote monitoring system. The feasibility of the system was evaluated by conducting a pilot study. Materials and Methods: The remote monitoring system consisted of a head and neck phantom and a user manual. The phantom contains a target and three OARs (organs at risk) that can be detected on CT images. TLD capsules were inserted at the center of the target and at the OARs. Two film slits for GafchromicEBT film were located on the axial and saggital planes. The user manual contained an IMRT planning guide and instructions for IMRT planning and the delivery process. After the manual and phantom were sent to four institutions, IMRT was planed and delivered. Predicted doses were compared with measured doses. Dose distribution along the two straight lines that intersected at the center of the axial film was measured and compared with the profiles predicted by the plan. Results: The measurements at the target agreed with the predicted dose within a 3% deviation. Doses at the OARs that represented the thyroid glands showed larger deviations (minimum 3.3% and maximum 19.8%). The deviation at OARs that represented the spiral cord was $0.7{\sim}1.4%$. The percentage of dose distributions that showed more than a 5% of deviation on the lines was $7{\sim}27%$ and $7{\sim}14%$ along the horizontal and vertical lines, respectively. Conculsion: Remote monitoring of IMRT using the developed system was feasible. With remote monitoring, the deviation at the target is expected to be small while the deviation at the OARs can be very large. Therefore, a method that is able to investigate the cause of a large deviation needs to be developed. In addition, a more clinically relevant measure for the two-dimensional dose comparison and pass/fail criteria need to be further developed.