Search Word(s) in Title, Keywords, Authors, and Abstract: Computed Tomography

Compression and Enhancement of Medical Images using Opposition Based Harmony Search Algorithm
Rekha Haridoss and Samundiswary Punniyakodi
Page: 288~304, Vol. 15, No.2, 2019
10.3745/JIPS.02.0108
Keywords: Entropy, Harmony Search Algorithm, Image Compression, Multi-Thresholding, Optimization
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The growth of telemedicine-based wireless communication for images—magnetic resonance imaging (MRI) and computed tomography (CT)—leads to the necessity of learning the concept of image compression. Over the years, the transform based and spatial based compression techniques have attracted many types of researches and achieve better results at the cost of high computational complexity. In order to overcome this, the optimization techniques are considered with the existing image compression techniques. However, it fails to preserve the original content of the diagnostic information and cause artifacts at high compression ratio. In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended with the optimization algorithm to compress the medical images effectively. However, the method becomes time consuming during the measurement of the randomness from the image pixel group and not suitable for medical applications. Hence, an attempt has been made in this paper to develop an HMT based image compression by utilizing the opposition based improved harmony search algorithm (OIHSA) as an optimization technique along with the entropy. Further, the enhancement of the significant information present in the medical images are improved by the proper selection of entropy and the number of thresholds chosen to reconstruct the compressed image.


3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment
Koojoo Kwon and Byeong-Seok Shin
Page: 1126~1134, Vol. 13, No.5, 2017
10.3745/JIPS.02.0070
Keywords: Segmentation, Large-Scale Image, Photo Editing, Visible Korean, Volume Rendering
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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.


Simulation of the Digital Image Processing Algorithm for the Coating Thickness Automatic Measurement of the TRISO-coated Fuel Particle
Woong-Ki Kim, Young-Woo Lee and Sung-Woong Ra
Page: 36~40, Vol. 1, No.1, 2005
None
Keywords: TRISO-coated Fuel Particle, Coating Thickness, X-ray CT, Computed Tomography, Filtered Backprojection, Automatic Measurement
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TRISO (Tri-Isotropic)-coated fuel particle is widely applied due to its higher stability at high temperature and its efficient retention capability for fission products in the HTGR (high temperature gas-cooled reactor), one of the highly efficient Generation IV reactors. The typical balltype TRISO-coated fuel particle with a diameter of about 1 mm is composed of a nuclear fuel particle as a kernel and of outer coating layers. The coating layers consist of a buffer PyC, inner PyC, SiC, and outer PyC layer. In this study, a digital image processing algorithm is proposed to automatically measure the thickness of the coating layers. An FBP (filtered backprojection) algorithm was applied to reconstruct the CT image using virtual X-ray radiographic images for a simulated TRISO-coated fuel particle. The automatic measurement algorithm was developed to measure the coating thickness for the reconstructed image with noises. The boundary lines were automatically detected, then the coating thickness was circularly by the algorithm. The simulation result showed that the measurement error rate was less than 1.4%.