Intelligent splitting of volume data

1 . A method for intelligent splitting of volume data, comprising:

receiving at least one of group order scan data and whole body scan data;

detecting global features in the received scan data;

defining separation lines relative to the detected features along an axis of the scan data; and

splitting the scan data into a plurality of data sets in accordance with the defined separation lines.

2 . A method as defined in claim 1 , further comprising:

interpolating between defined separation lines to locate other boundary lines of interest; and

splitting the scan data into a plurality of data sets in accordance with the interpolated lines.

3 . A method as defined in claim 1 wherein the plurality of data sets is partially overlapping.

4 . A method as defined in claim 1 , detecting global features comprising:

extracting at least one of feature points, contours, and regions from the volume data; and

using at least one of the feature points, contours, and regions as landmarks.

5 . A method as defined in claim 4 wherein the landmarks:

are robust against noise and variations;

are prominent and reliable; and

cover the key points in the complete volume data.

6 . A method as defined in claim 4 , detecting global features further comprising:

interpolating breakpoints between the landmarks; and

extracting a data section from the whole volume data in accordance with the interpolated breakpoints.

7 . A method as defined in claim 1 , detecting global features comprising:

constructing statistical models of at least one of external regions and internal organs of the human body;

identifying at least one region or organ responsive to the model;

performing model-based segmentation to reliably detect locations of the at least one region or organ in the volume data; and

extracting separation lines of the statistical model construction responsive to the segmentation.

8 . A method as defined in claim 1 , detecting global features comprising:

profiling a one-dimensional array of statistics, where the size of the array is relative to the number of slices in the axial direction, and the statistics are responsive to at least one of cross-sectional area and the sum of intensities within a slice; and

analyzing the profile to identify break lines of significance.

9 . A method as defined in claim 8 wherein the statistics comprise the intensity sum of all high-intensity pixels within each slice, and high-intensity pixels are defined as those higher than a predefined value.

10 . A method as defined in claim 9 wherein the predefined value is indicative of bone pixels.

11 . A system for intelligent splitting of volume data, comprising:

an adapter unit for receiving at least one of group order scan data and whole body scan data;

a feature detection unit in signal communication with the adapter unit for detecting global features in the received scan data, and for defining separation lines relative to the detected features along an axis of the scan data; and

a data splitting unit in signal communication with the adapter unit for splitting the scan data into a plurality of data sets in accordance with the defined separation lines.

12 . A system as defined in claim 11 , the feature detection unit comprising interpolation means for interpolating between defined separation lines to locate other boundary lines of interest.

13 . A system as defined in claim 11 , the feature detection unit comprising:

extraction means for extracting at least one of feature points, contours, and regions from the volume data; and

landmark means for using at least one of the feature points, contours, and regions as landmarks.

14 . A system as defined in claim 13 , the feature detection unit further comprising:

breakpoint interpolation means for interpolating breakpoints between the landmarks; and

breakpoint extraction means for extracting a data section from the whole volume data in accordance with the interpolated breakpoints.

15 . A system as defined in claim 11 , the feature detection unit comprising:

modeling means for constructing statistical models of at least one of external regions and internal organs of the human body;

identification means for identifying at least one region or organ responsive to the model;

segmentation means for performing model-based segmentation to reliably detect locations of the at least one region or organ in the volume data; and

separation means for extracting separation lines of the statistical model construction responsive to the segmentation.

16 . A system as defined in claim 11 , the feature detection unit comprising:

profile means for profiling a one-dimensional array of statistics, where the size of the array is relative to the number of slices in the axial direction, and the statistics are responsive to at least one of cross-sectional area and the sum of intensities within a slice; and

identification means for analyzing the profile to identify break lines of significance.

17 . A system as defined in claim 16 , the profile means comprising thresholding means for selecting the intensity sum of all high-intensity pixels within each slice, where the high-intensity pixels are defined as those higher than a predefined value.

18 . A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform program steps for intelligent splitting of volume data, the program steps comprising:

receiving at least one of group order scan data and whole body scan data;

detecting global features in the received scan data;

defining separation lines relative to the detected features along an axis of the scan data; and

splitting the scan data into a plurality of data sets in accordance with the defined separation lines.

19 . A device as defined in claim 18 , the program step of detecting global features comprising:

constructing statistical models of at least one of external regions and internal organs of the human body;

identifying at least one region or organ responsive to the model;

performing model-based segmentation to reliably detect locations of the at least one region or organ in the volume data; and

extracting separation lines of the statistical model construction responsive to the segmentation.

20 . A device as defined in claim 18 , the program step of detecting global features comprising:

profiling a one-dimensional array of statistics, where the size of the array is relative to the number of slices in the axial direction, and the statistics are responsive to at least one of cross-sectional area and the sum of intensities within a slice; and

analyzing the profile to identify break lines of significance.