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Based on the CT Images in Terms in the Four Kinds of Lung Segmentation Algorithm Performance Evaluation Studies

Author: ShiPuHua
Tutor: WangChangYuan
School: Taishan Medical College
Course: Medical Imaging and Nuclear Medicine
Keywords: otsu Morphology Connected region Clustering Growth law Uniformity within the region Inter-regional contrast
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 91
Quote: 1
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Abstract


Purpose four different algorithms lung CT images of the lung parenchyma segmentation , the main purpose is the realization of the lung parenchyma segmentation algorithm , and to explore the effect of the four algorithms segmentation algorithm performance evaluation criteria . By 166 different lung CT image , image processing, calculation and related parameters of the segmentation algorithm Evaluation Standard algorithm written by the author to explore the effect of the four algorithms . These algorithms are : based of otsu and morphology combined method , based on regional connectivity based clustering method , based on the growth . The use of intra-regional uniformity and regional quantitative analysis to split after the image contrast these two segmentation algorithm evaluation . Finally, these two indicators , the four lung segmentation algorithm for quantitative analysis and comparison . Results of the lung parenchyma CT image segmentation results show that , based on a combination of otsu and morphological connected region based clustering and four algorithms based on the region growing method , based on the evaluation of the segmentation algorithm , that inter-regional contrast uniformity and intra-regional comparison, the area of the four algorithms contrast between the mean value is based on the method of combining otsu and morphological , and based on the method of combining otsu and morphological stability is best. Contrast between the region 's stability , the stability of the four algorithms from good to bad in the order ( variance the smaller the better stability ) : based otsu and morphology combined ( variance 0.0116 ) lt ; the clustering kmean ( variance 0.0130 ) lt ; connected region ( variance 0.0181 ) lt ; based on regional growth ( variance 0.0287 ) . Conclusion four algorithms region contrast between a maximum value is based on the method of combining otsu and morphology , and based otsu and morphological stability of the method is the best for the lung parenchyma extract this specific application field , , the greater the contrast between the segmentation algorithm to extract the region more conducive for us to extract accurate lung parenchyma , and conducive to further extract ROI area .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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