Automatic detection of lung nodules from high resolution CT images
First Claim
1. A method for automatically detecting lung nodules from Multi-Slice High Resolution Computed Tomography (MSHR CT) images, comprising the steps of:
- defining a volume of interest (VOI) for moving through a lung volume in an MSHR CT image, based on MSHR CT image data;
examining the lung volume using the VOI, including, determining a local histogram of intensity inside the VOI; and
determining adaptive threshold values for segmenting the VOI to obtain seeds;
examining each of the seeds to detect the lung nodules therefrom, including, segmenting anatomical structures represented by the seeds by applying a segmentation method to the seeds that adaptively adjusts a segmentation threshold value based on a local histogram analysis of the seeds to extract the anatomical structures based on three-dimensional connectivity and intensity information corresponding to the local histogram; and
classifying each of the segmented, anatomical structures as one of a lung nodule or a non-nodule, based on a priori knowledge corresponding to the lung nodules and related, pre-defined anatomical structures;
displaying the lung nodules; and
analyzing the lung nodules, including, automatically quantifying features of the lung nodules to provide an automatic detection decision for each of the lung nodules.
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Abstract
A method for automatically detecting lung nodules from MSHR CT images includes defining a volume of interest (VOI) for a lung volume in an MSHR CT image. The lung volume is examined using the VOI, including, determining a local histogram of intensity and adaptive threshold values for segmenting the VOI to obtain seeds. Each seed is examined to detect lung nodules therefrom, including segmenting anatomical structures represented by the seed by applying a segmentation method that adaptively adjusts a segmentation threshold value based on histogram analysis of the seed to extract the structures based on three-dimensional connectivity and histogram intensity information, and classifying each structure as a lung nodule or a non-nodule based on a priori knowledge corresponding to lung nodules and related structures. The lung nodules are displayed. The lung nodules are analyzed, including automatically quantifying lung nodule features to provide an automatic detection decision.
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Citations
32 Claims
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1. A method for automatically detecting lung nodules from Multi-Slice High Resolution Computed Tomography (MSHR CT) images, comprising the steps of:
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defining a volume of interest (VOI) for moving through a lung volume in an MSHR CT image, based on MSHR CT image data;
examining the lung volume using the VOI, including, determining a local histogram of intensity inside the VOI; and
determining adaptive threshold values for segmenting the VOI to obtain seeds;
examining each of the seeds to detect the lung nodules therefrom, including, segmenting anatomical structures represented by the seeds by applying a segmentation method to the seeds that adaptively adjusts a segmentation threshold value based on a local histogram analysis of the seeds to extract the anatomical structures based on three-dimensional connectivity and intensity information corresponding to the local histogram; and
classifying each of the segmented, anatomical structures as one of a lung nodule or a non-nodule, based on a priori knowledge corresponding to the lung nodules and related, pre-defined anatomical structures;
displaying the lung nodules; and
analyzing the lung nodules, including, automatically quantifying features of the lung nodules to provide an automatic detection decision for each of the lung nodules. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 29, 30, 32)
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20. A system for automatically detecting lung nodules from Multi-Slice High Resolution Computed Tomography (MSHR CT) images, comprising the steps of:
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a volume of interest selector for defining a volume of interest (VOI) based on MSHR CT image data corresponding to an MSHR CT image, the VOI for moving through a lung volume in the MSHR CT image;
a lung volume examination device for determining a local histogram of intensity inside the VOI, and for determining adaptive threshold values for segmenting the VOI to obtain seeds;
a seed examination device for examining each of the seeds to detect the lung nodules therefrom, including, a segmentation device for segmenting anatomical structures represented by the seeds by applying a segmentation method to the seeds that adaptively adjusts a segmentation threshold value based on a local histogram analysis to extract the anatomical structures based on three-dimensional connectivity and intensity information corresponding to the local histogram; and
a classifier for classifying each of the segmented, anatomical structures as one of a lung nodule or a non-nodule, based on a priori knowledge corresponding to the lung nodules and related, pre-defined anatomical structures;
a display device for displaying the lung nodules; and
a detection device for automatically quantifying features of the lung nodules to provide an automatic detection decision for each of the lung nodules. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 31)
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Specification