Hu Xiaofang, Xu Feng, Wang Ming, Wu Xiaoping

The International Conference on Computational & Experimental Engineering and Sciences, Vol.18, No.4, pp. 119-120, 2011, DOI:10.3970/icces.2011.018.119

Abstract The Synchrotron Radiation X-ray Computed Tomography (SR-CT) technique is a non- destructive detection technology which can give the in-situ observation of microstructure evolution of materials under the external field (e.g., high pressure, high temperature, electromagnetic field, intense radiation, etc.), and it has a significant application in the area of plants and crops, advanced manufacturing, advanced materials, biomedicine, mechanics, archaeology and so on. More >

Y. Gür^*

Molecular & Cellular Biomechanics, Vol.11, No.4, pp. 249-258, 2014, DOI:10.3970/mcb.2014.011.249

Abstract The purpose of the study presented here was to investigate the manufacturability of human anatomical models from Computed Tomography (CT) scan data via a 3D desktop printer which uses fused deposition modelling (FDM) technology. First, Digital Imaging and Communications in Medicine (DICOM) CT scan data were converted to 3D Standard Triangle Language (STL) format by using InVaselius digital imaging program. Once this STL file is obtained, a 3D physical version of the anatomical model can be fabricated by a desktop 3D FDM printer. As a case study, a patient’s skull CT scan data was considered, and a tangible version of… More >

Zhe Liu¹, Charlie Maere^1,*, Yuqing Song¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes… More >

S. Gopalakrishnan^{1, *}, A. Kandaswamy²

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 141-152, 2018, DOI:10.3970/cmes.2018.114.141

Abstract Delineation of the lung parenchyma in the thoracic Computed Tomography (CT) is an important processing step for most of the pulmonary image analysis such as lung volume extraction, lung nodule detection and pulmonary vessel segmentation. An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper. The proposed method involves a segmentation phase followed by a lung boundary correction technique. The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians. We propose a histogram utilized Adaptive Multilevel Thresholding (AMT) for estimating the total number of Gaussians and… More >