System and method for detecting ground glass nodules in medical images

Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image po...

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Main Authors Hong, Lin, Lu, Yumao, Shen, Hong
Format Patent
LanguageEnglish
Published 30.06.2009
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Abstract Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image point belonging to a ground glass nodule from the variation range of the background tissue probability distribution function by using a function corresponding to the variation range of the background tissue probability distribution function, and calculating a log likelihood function of the image from the background tissue probability distribution function and the estimated ground glass nodule probability distribution function, wherein the log likelihood function represents the confidence that a point in the image is not part of a ground glass nodule. The log likelihood function value for each point is compared to a confidence value of the background tissue, to determine if the point is a candidate ground glass nodule location.
AbstractList Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image point belonging to a ground glass nodule from the variation range of the background tissue probability distribution function by using a function corresponding to the variation range of the background tissue probability distribution function, and calculating a log likelihood function of the image from the background tissue probability distribution function and the estimated ground glass nodule probability distribution function, wherein the log likelihood function represents the confidence that a point in the image is not part of a ground glass nodule. The log likelihood function value for each point is compared to a confidence value of the background tissue, to determine if the point is a candidate ground glass nodule location.
Author Hong, Lin
Lu, Yumao
Shen, Hong
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References Zhang Li et al., "Consistent Interactive Segmentation of Pulmonary Ground Glass Nodules Identified in CT Studies," Proceedings of the SPIE (2004) 5370, pp. 1709-1719.
Fan I et al., "Automated Detection of Lung Nodules From Multi-Slice Low-Dose CT Images," Proceedings of the SPIE (2001) 4322 pp. 1828-1835.
Leemput, Koen Van et al., "Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection," IEEE Transactions Medical Imaging, (2001) 20:8 p. 677.
Li et al. (6937776) 20050800
Zhang et al. (7209581) 20070400
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