• Imaging Science in Dentistry
• /
• 제34권1호
• /
• pp.13-18
• /
• 2004

Purpose : This study was to evaluate the influence of slice thickness of computed tomography (CT) and rapid protyping (RP) type on the accuracy of 3-dimensional medical model. Materials and Methods: Transaxial CT data of human dry skull were taken from multi-detector spiral CT. Slice thickness were 1, 2, 3 and 4 mm respectively. Three-dimensional image model reconstruction using 3-D visualization medical software (V-works /sup TM/ 3.0) and RP model fabrications were followed. 2-RP models were 3D printing (Z402, Z Corp., Burlington, USA) and Stereolithographic Apparatus model. Linear measurements of anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared according to slice thickness and RP model type. Results: There were relative error percentage in absolute value of 0.97, 1.98,3.83 between linear measurements of dry skull and image models of 1, 2, 3 mm slice thickness respectively. There was relative error percentage in absolute value of 0.79 between linear measurements of dry skull and SLA model. There was relative error difference in absolute value of 2.52 between linear measurements of dry skull and 3D printing model. Conclusion: These results indicated that 3-dimensional image model of thin slice thickness and stereolithographic RP model showed relative high accuracy.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2006년도 춘계학술대회 논문집
• /
• pp.175-176
• /
• 2006

The aim of the 3D medical imaging is to facilitate the creation of clinically usable image-based algorithm. Clinically usable imaging algorithm for image analysis requires a high degree of interaction to verify and correct results from registration algorithms, such as the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) which are the class libraries. ITK provides segmentation algorithms and VTK has powerful 3D visualization. However, to apply those libraries to the medical images such as Computerized Tomography (CT), the algorithm based on the interactive construction and modification of data objects are necessary. In this paper we showed the 3D registration about mandibular premolar of human teeth acquired by micro-CT scanner. Also, we used the ITK to find the contour of pulp layer of premolar, furthermore, the 3D imaging was visualized with VTK designed to create one kind of view on the data of 3D visualization. Finally, we evaluated that the volume model of pulp layer would be useful for the tooth morphology in dental medicine.

• Journal of Computing Science and Engineering
• /
• 제4권4호
• /
• pp.368-387
• /
• 2010

We propose a novel method for the registration of 3D CT scans to 2D endoscopic images during the image-guided medialization laryngoplasty. This study aims to allow the surgeon to find the precise configuration of the implant and place it into the desired location by employing accurate registration methods of the 3D CT data to intra-operative patient and interactive visualization tools for the registered images. In this study, the proposed registration methods enable the surgeon to compare the outcome of the procedure to the pre-planned shape by matching the vocal folds in the CT rendered images to the endoscopic images. The 3D image fusion provides an interactive and intuitive guidance for surgeon by visualizing a combined and correlated relationship of the multiple imaging modalities. The 3D Magic Lens helps to effectively visualize laryngeal anatomical structures by applying different transparencies and transfer functions to the region of interest. The preliminary results of the study demonstrated that the proposed method can be readily extended for image-guided surgery of real patients.

• 한국정밀공학회지
• /
• 제30권9호
• /
• pp.991-999
• /
• 2013

Lower limbs deformity is a congenital disease and can also be occurred by an acquired factor. This paper suggests a new technique for surgical planning of Corrective Osteotomy for Lower Limbs (COLL) using 2D-3D medical image registration. Converting to a 3D modeling data of lower limb based on CT (computed tomography) scan, and divide it into femur, tibia and fibula; which composing the lower limb. By rearranging the model based on the biplane 2D images of X-ray data, a 3D upright bone structure was acquired. There are two ways to array the 3D data on the 2D image: Intensity-based registration and feature-based registration. Even though registering Intensity-based method takes more time, this method will provide more precise results, and will improve the accuracy of surgical planning.

• 대한의용생체공학회:의공학회지
• /
• 제27권6호
• /
• pp.392-401
• /
• 2006

Image Guided Surgery (IGS) system which has variously tried in medical engineering fields is able to give a surgeon objective information of operation process like decision making and surgical planning. This information is displayed through 3D images which are acquired from image modalities like CT and MRI for pre-operation. The technique of image registration is necessary to construct IGS system. Image registration means that 3D model and the object operated by a surgeon are matched on the common frame. Major techniques of registration in IGS system have been used by recognizing fiducial markers placed on the object. However, this method has been criticized due to additional trauma, its invasive protocol inserting fiducial markers in patient's bone and generating noise data when 2D slice images are acquired by image modality because many markers are made of metal. Therefore, this paper developed shape-based registration technique to improve the limitation of fiducial marker based IGS system. Iterative Closest Points (ICP) algorithm was used to match corresponding points and quaternion based rotation and translation transformation using closed form solution applied to find the optimized cost function of transformation. we assumed that this algorithm were used in Total Knee replacement (TKR) operation. Accordingly, we have developed region-based 3D registration technique based on anatomical landmarks and this registration algorithm was evaluated in a femur model. It was found that region-based algorithm can improve the accuracy in 3D registration.

• Journal of Multimedia Information System
• /
• 제3권3호
• /
• pp.47-52
• /
• 2016

This research paper examines the usage of graphic imaging in Holographic Projections to further advance the medical field. It highlights the importance and necessity of this technology as well as avant-garde techniques applied in the process of displaying images in digital holography. This paper also discusses the different types of applications for holograms in society today. Different tools were utilized to transfer a set of a cancer patient's brain tumor data into data used to produce a 3D holographic image. This image was produced through the transfer of data from one program to another. Through the use of semi-automatic segmentation through the seed region method, we were able to create a 3D visualization from Computed Tomography (CT) data.

• 대한방사성의약품학회지
• /
• 제3권2호
• /
• pp.65-71
• /
• 2017

To assess the effects of filter and reconstruction of Cu-64 PET data on Siemens scanner, the various reconstruction algorithm with various filters were assessed in terms of spatial resolution, non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR). Image reconstruction was performed using filtered backprojection (FBP), 2D ordered subset expectation maximization (OSEM), 3D reprojection algorithm (3DRP), and maximum a posteriori algorithms (MAP). For the FBP reconstruction, ramp, butterworth, hamming, hanning, or parzen filters were used. Attenuation or scatter correction were performed to assess the effect of attenuation and scatter correction. Regarding spatial resolution, highest achievable volumetric resolution was $3.08mm^3$ at the center of FOV when MAP (${\beta}=0.1$) reconstruction method was used. SOR was below 4% for FBP when ramp, Hamming, Hanning, or Shepp-logan filter were used. The lowest NU (highest uniform) after attenuation & scatter correction was 5.39% when FBP (parzen filter) was used. Regarding RC, 0.9 < RC < 1.1 was obtained when OSEM (iteration: 10) was used when attenuation and scatter correction were applied. In this study, image quality of Cu-64 on Siemens Inveon PET was investigated. This data will helpful for the quantification of Cu-64 PET data.

• 한국컴퓨터그래픽스학회논문지
• /
• 제3권2호
• /
• pp.27-34
• /
• 1997

3 차원 영상 모델링은 자동 시각적 검사와, 비파괴 검사분야에서 절실히 요구되고 있는 연구 분야이다. 또한 그것은 생의학연구, 의료, 수술계획과 정교성이 요구되는 중대한 수술 (안면 절개) 등에 매우 유용하다. 영상처리 및 분석 기술은 3 차원 의료 영상 정보의 질올 높여 주는데, 의료정보를 정확하고 빠르게 분석하는 일은 용이하지 않다. 본 논문에서는 향상된 3 차원 의료영상의 가시화를 위하여 사면체 분할법에 의한 모델링 방법을 제안한다. 이 방법에서는 트라이 베리에이트 구간별 선형 보간법이 구축된 사면체영역에 걸쳐 적용된다. 그리고, 등면, 색채 윤곽, 슬라이싱 등 가시화 방법들도 논의된다. 이것은 마칭큐브스 알고리즘으로 인해 제기되는 불확실한 경우가 발생하지 않고, 자료 감축의 효과도 가져올 수 있으므로 보다 정확하고 빠른 의료정보 분석에 기여할 수 있을 것으로 사료된다. 그리고, 자료 감축으로 인한 정확도의 감소가 발생할 경우에는 최소제곱을 바탕으로 한 사면체 세분할을 사용하여 보완할 수 있을 것으로 기대한다.

• Journal of Information Processing Systems
• /
• 제13권5호
• /
• pp.1126-1134
• /
• 2017

A variety of medical service applications in the field of the Internet of Things (IoT) are being studied. Segmentation is important to identify meaningful regions in images and is also required in 3D images. Previous methods have been based on gray value and shape. The Visible Korean dataset consists of serially sectioned high-resolution color images. Unlike computed tomography or magnetic resonance images, automatic segmentation of color images is difficult because detecting an object's boundaries in colored images is very difficult compared to grayscale images. Therefore, skilled anatomists usually segment color images manually or semi-automatically. We present an out-of-core 3D segmentation method for large-scale image datasets. Our method can segment significant regions in the coronal and sagittal planes, as well as the axial plane, to produce a 3D image. Our system verifies the result interactively with a multi-planar reconstruction view and a 3D view. Our system can be used to train unskilled anatomists and medical students. It is also possible for a skilled anatomist to segment an image remotely since it is difficult to transfer such large amounts of data.

• 대한의용생체공학회:의공학회지
• /
• 제44권4호
• /
• pp.255-263
• /
• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.