3D microscopy images contain abundant astronomical data, rendering 3D microscopy image processing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of interest (ROI) of the input image to a small size. Although this reduces cost and time, there are drawbacks at the image processing level, e.g., the selected ROI strongly depends on the user and there is a loss in original image information. To mitigate these problems, we developed a 3D microscopy image processing tool on a graphics processing unit (GPU). Our tool provides efficient and various automatic thresholding methods to achieve intensity-based segmentation of 3D microscopy images. Users can select the algorithm to be applied. Further, the image processing tool provides visualization of segmented volume data and can set the scale, transportation, etc. using a keyboard and mouse. However, the 3D objects visualized fast still need to be analyzed to obtain information for biologists. To analyze 3D microscopic images, we need quantitative data of the images. Therefore, we label the segmented 3D objects within all 3D microscopic images and obtain quantitative information on each labeled object. This information can use the classification feature. A user can select the object to be analyzed. Our tool allows the selected object to be displayed on a new window, and hence, more details of the object can be observed. Finally, we validate the effectiveness of our tool by comparing the CPU and GPU processing times by matching the specification and configuration.
We designed hybrid x-ray detector and simulated using Monte Carlo method. Hybrid x-ray detectors consist of scintillator coupled photoconductor structure. In the hybrid structure, x-ray photons are converted into the light photon in the scintillator layer and light photons are converted into the electric charge in the semiconductor layer. The electric charges can be generated from directly x-ray absorption in the semiconductor material. We design the columnar CsI:Na as scintillator layer and a-Se as photoconductor material. When x-ray photon incident the scintillator layer, the photons are distributed through the scintillator, and then generated light photon influence the semiconductor material. We study the light photon distribution according to the scintillator layer thickness and the detector pixel size which have influence on image resolution.
KEYWORDS: Motion models, Single photon emission computed tomography, Visualization, Heart, Magnetic resonance imaging, Motion analysis, Image segmentation, Medical imaging, Visual process modeling, C++
Representation methods for cardiac motility were developed in this study. We estimated some parameters which have cardiac feature to model with an innovative scheme. The parameterized super quadric model to visualize the motion of a left ventricle was implemented with OpenGL and Visual C++. Myocardial wall thickening was displayed with super-ellipsoidal model. The measured count for thickening was changed as time frames in this model. And motility was parameterized additionally in the parameterized super quadric model. We made an experiment on analyzing the motility of left ventricle myocardium. The criterion was tested in the validation study in 7 normal subjects and 26 patients with prior myocardial infarction. In order to analyze the motility, we used mean and variance of the total motion during cardiac cycle. The average of normal subject has 0.46 and variance has 0.02. In the case of patients, the average and variance of motility has 0.59 and 0.08 respectively. Although the average value didn’t have the difference between normal and abnormal, the variance had them. In general, patients were 0.08 and normal subjects were 0.02 in variance. The difference between normal subjects and abnormal subjects was estimated. In abnormal subject, the motility was 128% higher than normal subject. The variance was also 328% high. In the patient study, the quantity of motion is decreased rapidly in stressed states. In the visualization for contractility, fifteen segment variables were displayed. The locations of all point could be rotation with mouse interface. The most of factors were visualized for cardiac motility and cardiac features. We expect that this model distinguishes between normal subjects and abnormal subjects. And an exact analysis of momentum utilizing this model could be evaluated.
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