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Methods and apparatus are configured to provide data to render (medical) images using direct volume rendering by electronically analyzing a medical volume data set associated with a patient that is automatically electronically divided into a plurality of local histograms having intensity value range

Methods and apparatus are configured to provide data to render (medical) images using direct volume rendering by electronically analyzing a medical volume data set associated with a patient that is automatically electronically divided into a plurality of local histograms having intensity value ranges associated therewith and programmatically generating data used for at least one of tissue detection or tissue classification of tissue having overlapping intensity values.

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That which is claimed: 1. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local histograms; automatically electronically analyzing a plurality of the local histograms; programmatically d

That which is claimed: 1. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local histograms; automatically electronically analyzing a plurality of the local histograms; programmatically distinguishing different materials with overlapping intensity values using at least one range weight data value, wherein the at least one range weight data value is derived as the portion of the pixel or voxel intensity values in the local histogram being within a predefined partial range wherein the at least one range weight data value is used to electronically establish whether a sufficient portion of each local histogram is within a defined partial range, and wherein the programmatically distinguishing step uses the at least one range weight data value to determine whether a particular type of material is associated with different neighborhoods having at least some intensity values inside the defined partial range such that the material can be (a) associated with some of the neighborhoods having less than all of the intensity values inside the partial range with a range weight data value below 100% but above a threshold value and (b) not associated with some of the neighborhoods having some of the intensity values inside the partial range with a range weight data value above 0% but below a threshold value; generating at least one of a visualization attribute or visualization parameters for the pixels or voxels based on the range weight analysis of local neighborhoods used in the programmatically distinguishing step; rendering an image with the programmatically distinguished different materials using at least one of the generated visualization attribute or the generated visualization parameters; and programmatically generating at least one partial range histogram of data based on the analyzing step to carry out the distinguishing the materials step, wherein the partial range histogram is populated based on the at least one range weight data value and includes local neighborhoods that have a sufficient number of voxels with intensity values within the defined partial range and can include local neighborhoods having one or more voxel intensity values outside the partial range and can exclude local neighborhoods that have voxel intensity values inside the partial range. 2. A method according to claim 1, wherein a respective partial range histogram can contain local and distributed voxels having similar range weights. 3. A method according to claim 1, further comprising selectively applying a trapezoid transfer function to at least one partial range of interest to thereby enhance visualization of a feature in a rendered image. 4. A method according to claim 1, further comprising automatically applying trapezoid transfer functions to the partial range histograms to render the image. 5. A method according to claim 1, further comprising electronically generating a first adaptive color-opacity trapezoid as a transfer function component that adapts center, width and shape to respective partial range histograms. 6. A method according to claim 1, further comprising: allowing a user to electronically browse detected partial range histograms and select one or more partial range histograms of interest, wherein the partial range histograms include local neighborhoods that have a sufficient number of voxels with intensity values within a defined partial range based on the at least one range weight data value and can include neighborhoods of voxel intensity values outside the partial range and can exclude neighborhoods with voxel intensity values within the partial range; then electronically define a transfer function for the selected one or more partial range histograms to generate the rendered image. 7. A method according to claim 1, further comprising automatically detecting peak characteristics in the partial range histograms and merging selected partial range histograms having similar peak characteristics to define merged partial range histograms. 8. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local intensity voxel/pixel histograms; allowing a user to electronically select a partial range of interest in the volume data set; automatically electronically analyzing a plurality of the local histograms; programmatically generating a partial range histogram using the user-selected partial range of interest and at least one range weight value, wherein the partial range histogram includes local neighborhoods that have a sufficient number of voxels with intensity values within the user-selected partial range and wherein the partial range histogram can (a) include local neighborhoods with voxel/pixel intensity values outside the partial range and (b) exclude local neighborhoods with voxel/pixel intensity values inside the partial range; applying an adaptive trapezoid transfer function to the partial range histogram; and electronically visually emphasizing tissue in a rendering in response to the application of the adaptive trapezoid. 9. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local histograms; automatically electronically analyzing a plurality of the local histograms; programmatically distinguishing different materials with overlapping intensity values using at least one range weight data value, wherein the at least one range weight data value is derived as the portion of the pixel or voxel intensity values in the local histogram being within a predefined partial range wherein the at least one range weight data value is used to electronically establish whether a sufficient portion of each local histogram is within a defined partial range, and wherein the programmatically distinguishing step uses the at least one range weight data value to determine whether a particular type of material is associated with different neighborhoods having at least some intensity values inside the defined partial range such that the material can be (a) associated with some of the neighborhoods having less than all of the intensity values inside the partial range with a range weight data value below 100% but above a threshold value and (b) not associated with some of the neighborhoods having some of the intensity values inside the partial range with a range weight data value above 0% but below a threshold value; generating at least one of a visualization attribute or visualization parameters for the pixels or voxels based on the range weight analysis of local neighborhoods used in the programmatically distinguishing step; and rendering an image with the programmatically distinguished different materials using at least one of the generated visualization attribute or the generated visualization parameters, wherein the analyzing step comprises electronically allocating each local histogram to one of a plurality of partial range histograms based on whether a respective local neighborhood meets a threshold of the at least one range weight data value, wherein the partial range histograms include local neighborhoods that have a sufficient number of voxels with intensity values within a defined partial range and can include neighborhoods with voxel intensity values outside the partial range and can exclude neighborhoods with voxel intensity values within the partial range. 10. A method according to claim 9, wherein the electronically allocating is based on detected peak characteristics of voxel data in the local histograms. 11. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local histograms, wherein the volume rendering data set comprises MM data with an uncalibrated intensity scale; automatically electronically analyzing a plurality of the local histograms; programmatically distinguishing different materials with overlapping intensity values using at least one range weight data value, wherein the at least one range weight data value is derived as the portion of the pixel or voxel intensity values in the local histogram being within a predefined partial range wherein the at least one range weight data value is used to electronically establish whether a sufficient portion of each local histogram is within a defined partial range, and wherein the programmatically distinguishing step uses the at least one range weight data value to determine whether a particular type of material is associated with different neighborhoods having at least some intensity values inside the defined partial range such that the material can be (a) associated with some of the neighborhoods having less than all of the intensity values inside the partial range with a range weight data value below 100% but above a threshold value and (b) not associated with some of the neighborhoods having some of the intensity values inside the partial range with a range weight data value above 0% but below a threshold value; and generating at least one of a visualization attribute or visualization parameters for the pixels or voxels based on the range weight analysis of local neighborhoods used in the programmatically distinguishing step; and rendering an image with the programmatically distinguished different materials using at least one of the generated visualization attribute or the generated visualization parameters, wherein the MRI data comprises MR angiography (MRA) data, and wherein the method further comprises adapting a transfer function to allow relatively precise manual diameter measurements of the aortic diameter using data from partial range histograms, wherein the partial range histograms are populated based on the range weight data values and can include local neighborhoods that have a sufficient number of voxels with intensity values within a defined partial range and can include local neighborhoods with voxel intensity values outside the partial range and can exclude local neighborhoods with voxel intensity values within the partial range. 12. A method according to claim 11, further comprising automatically detecting a contrast agent in the MRA data and automatically adjusting parameters for volume rendering of the MRA angiography data set. 13. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into local histograms; automatically electronically analyzing a plurality of the local histograms; programmatically distinguishing different materials with overlapping intensity values using range weight data values obtained from the electronic analysis of the local histograms, wherein the range weight data values are used to electronically establish whether a sufficient portion of each local histogram is within a defined partial range; rendering an image with the programmatically distinguished different materials; electronically defining partial range histograms based on identified peak characteristics in the local histograms, wherein the partial range histograms include local neighborhoods that have a sufficient number of voxels with intensity values within a the defined partial range and can include neighborhoods with voxel intensity values outside the defined partial range; fitting a respective Gaussian curve to each of the partial range histograms; and generating adaptive trapezoidal transfer functions, one for each of the Gaussian curves of the partial range histograms. 14. A method of evaluating data associated with direct volume renderings, comprising: electronically subdividing a volume rendering data set into neighborhoods with local histograms; automatically electronically analyzing a plurality of the local histograms; programmatically distinguishing different materials with overlapping intensity values using at least one range weight data value, wherein the at least one range weight data value is derived as the portion of the pixel or voxel intensity values in the local histogram being within a predefined partial range wherein the at least one range weight data value is used to electronically establish whether a sufficient portion of each local histogram is within a defined partial range, and wherein the programmatically distinguishing step uses the at least one range weight data value to determine whether a particular type of material is associated with different neighborhoods having at least some intensity values inside the defined partial range such that the material can be (a) associated with some of the neighborhoods having less than all of the intensity values inside the partial range with a range weight data value below 100% but above a threshold value and (b) not associated with some of the neighborhoods having some of the intensity values inside the partial range with a range weight data value above 0% but below a threshold value; generating at least one of a visualization attribute or visualization parameters for the pixels or voxels based on the range weight analysis of local neighborhoods used in the programmatically distinguishing step; rendering an image with the programmatically distinguished different materials using at least one of the generated visualization attribute or the generated visualization parameters; and adapting at least one a priori transfer function to evaluate different volume rendering data sets of similar examination types using programmatically generated partial range histograms of the local histogram data, wherein the partial range histograms are generated using the at least one range weight data value and include local neighborhoods that have a sufficient number of voxels with intensity values within a defined partial range and can include neighborhoods with voxel intensity values outside the partial range and can exclude neighborhoods with voxel intensity values inside the partial range. 15. A method for providing a tissue exploration tool to allow a physician to interactively analyze medical volume data sets in a visualization system, comprising: allowing a user to electronically select a partial range of interest in an intensity scale of voxel data to thereby allow the user to interactively investigate voxels in a volume rendering data set; electronically generating a partial range histogram of local neighborhoods that have a sufficient number of voxels with intensity values within the selected partial range as defined by a range weight and can: (i) include local neighborhoods of voxel intensity values outside the selected partial range and (ii) exclude local neighborhoods with voxel intensity values inside the selected partial range; electronically fitting an adaptive trapezoid to the partial range histogram; and electronically rendering an image of material associated with the selected partial range and adaptive trapezoid. 16. A method according to claim 15, further comprising displaying a graphic interface that allows a user to electronically slide a partial range bar over a global histogram in the intensity scale, wherein the partial range histogram is generated using range weight data values of voxels in local neighborhoods that can be distributed through a volume. 17. A method for visualizing images of volume data sets, comprising: iteratively electronically subdividing a respective volume data set using local histograms of neighborhood voxel data to allocate the neighborhoods of data into partial range histograms, each partial range histogram includes neighborhoods of data having a sufficient number of voxels with intensity values in a defined range as defined by a range weight, and wherein a respective partial range histogram can include neighborhoods with voxel intensity values outside the defined range and can exclude neighborhoods with voxel intensity values inside the defined range; electronically automatically identifying different materials in the volume data set based on the partial range histograms, including materials having overlapping image intensity values which may be distributed over a target volume of interest; and electronically rendering an image of the identified different materials in the volume data set to a display. 18. A method according to claim 17, wherein the local neighborhoods are selected, configured and sized so that the volume data set can be analyzed with non-overlapping subdivision of the voxel data, wherein a respective partial range histogram can include local neighborhoods that are distributed throughout the volume data set. 19. A method according to claim 17, wherein the range weight is derived using the equation: where N is an arbitrary voxel neighborhood, VΦ is the set of voxels within a range Φ and |V| denotes the number of voxels in a set V. 20. A system for generating DVR medical images, comprising: a volume rendering medical image processor system with an electronic circuit configured to generate data for a diagnostic medical image of a target region of a patient by electronically subdividing a volume rendering data set using local histogram analysis to define a partial range histogram formed with a composite set of neighborhoods having a sufficient number of voxels or pixels with intensity values in a partial range as defined by a range weight, and wherein the partial range histogram can include local histograms with voxels or pixels outside the partial range and can exclude local histograms with voxels or pixels inside the partial range, and wherein the neighborhoods associated with the partial range histogram can be distributed through the volume rendering data set to separate different tissues with overlapping image intensity values. 21. A system according to claim 20, wherein the processor system is configured to analyze the partial range histograms and peak characteristics and intensity values associated with the local histogram voxels to electronically automatically identify different tissue with overlapping image intensity values. 22. A system according to claim 20, wherein the processor system is configured to programmatically perform at least one of: (a) classify and (b) detect tissues in the volume data set using data from the local histogram analysis. 23. computer readable storage medium having computer readable program code embodied in the medium, the computer-readable program code comprising: computer readable program code configured to generate partial range histograms having associated peak characteristics and intensity values to electronically identify different types of tissue having overlapping image intensity values, the partial range histograms comprising local neighborhoods of voxel intensity data that have a sufficient number of voxels with intensity values within the partial range as defined by a range weight, wherein the partial range histograms can include local neighborhoods with voxel intensity values outside the partial range and can exclude local neighborhoods with voxel intensity values inside the partial range; and computer readable program code configured to render a diagnostic medical image of different materials in a target region of a patient using data from the partial range histograms. 24. A computer readable storage medium according to claim 23, further comprising computer readable program code configured to define at least one range weight, wherein the computer readable program code that generates the partial range histograms is configured to use the at least one defined range weight to determine if a local neighborhood should be included or excluded from a respective partial range histogram.