Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images.

The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients' breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.

A prototype of radioactive waste drum monitor by non-destructive assays using gamma spectrometry.

In this work, segmented gamma scanning and the gamma emission tomography were used to locate unknown sources in a radioactive waste drum. The simulated detector response function and full energy peak efficiency are compared to corresponding experimental data and show about 5.3% difference for an energy ranging from 81keV to 1332.5keV for point sources. Computation of the corresponding activity is in good agreement with the true values.