Biomaterial Database

Automatic segmentation of liver PET images. 2008

Chih-Yu Hsu, and Chun-You Liu, and Chung-Ming Chen

Department of Information and Communication Engineering, Chaoyang University of Technology, Wufeng, Taichung County, Taiwan, ROC. tccnchsu@gmail.com

Automation of liver positron emission tomography (PET) image segmentation is proposed in this paper. A new active contour model (ACM), called Poisson Gradient Vector Flow (PGVF), with genetic algorithm (GA) constructs a scheme to automatically find the contour of liver in the PET images. PET is widely used for the clinical purpose, but image quality of PET makes the image segmentation be a tough work. Three image data sets are tested for evaluating the new segmentation approach of liver PET images. One image data set is adapted from the study of one person with a normal liver. The other two image data sets are adapted from the studies of two patients with abnormal livers. The results show that the regions of interest (ROI) of liver are automatically segmented from the images of three data sets.

UI	MeSH Term	Description	Entries
D007089	Image Enhancement	Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.	Image Quality Enhancement,Enhancement, Image,Enhancement, Image Quality,Enhancements, Image,Enhancements, Image Quality,Image Enhancements,Image Quality Enhancements,Quality Enhancement, Image,Quality Enhancements, Image
D007090	Image Interpretation, Computer-Assisted	Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.	Image Interpretation, Computer Assisted,Computer-Assisted Image Interpretation,Computer-Assisted Image Interpretations,Image Interpretations, Computer-Assisted,Interpretation, Computer-Assisted Image,Interpretations, Computer-Assisted Image
D008099	Liver	A large lobed glandular organ in the abdomen of vertebrates that is responsible for detoxification, metabolism, synthesis and storage of various substances.	Livers
D010363	Pattern Recognition, Automated	In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)	Automated Pattern Recognition,Pattern Recognition System,Pattern Recognition Systems
D011877	Radionuclide Imaging	The production of an image obtained by cameras that detect the radioactive emissions of an injected radionuclide as it has distributed differentially throughout tissues in the body. The image obtained from a moving detector is called a scan, while the image obtained from a stationary camera device is called a scintiphotograph.	Gamma Camera Imaging,Radioisotope Scanning,Scanning, Radioisotope,Scintigraphy,Scintiphotography,Imaging, Gamma Camera,Imaging, Radionuclide
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001185	Artificial Intelligence	Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.	AI (Artificial Intelligence),Computer Reasoning,Computer Vision Systems,Knowledge Acquisition (Computer),Knowledge Representation (Computer),Machine Intelligence,Computational Intelligence,Acquisition, Knowledge (Computer),Computer Vision System,Intelligence, Artificial,Intelligence, Computational,Intelligence, Machine,Knowledge Representations (Computer),Reasoning, Computer,Representation, Knowledge (Computer),System, Computer Vision,Systems, Computer Vision,Vision System, Computer,Vision Systems, Computer
D012680	Sensitivity and Specificity	Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)	Specificity,Sensitivity,Specificity and Sensitivity
D015203	Reproducibility of Results	The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.	Reliability and Validity,Reliability of Result,Reproducibility Of Result,Reproducibility of Finding,Validity of Result,Validity of Results,Face Validity,Reliability (Epidemiology),Reliability of Results,Reproducibility of Findings,Test-Retest Reliability,Validity (Epidemiology),Finding Reproducibilities,Finding Reproducibility,Of Result, Reproducibility,Of Results, Reproducibility,Reliabilities, Test-Retest,Reliability, Test-Retest,Result Reliabilities,Result Reliability,Result Validities,Result Validity,Result, Reproducibility Of,Results, Reproducibility Of,Test Retest Reliability,Validity and Reliability,Validity, Face

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