Patent Number: 060552956
Section: description

DETAILED DESCRIPTION OF THE INVENTION FIG. 1 schematically depicts one possible embodiment of a computerized x-ray imaging system 10 which employs an apparatus 38 for providing automatic collimation according to the present invention. The x-ray imaging system 10 is used for diagnostic imaging studies of the peripherals (e.g. legs, arms, neck, and head). The x-ray system 10 comprises a computerized imaging device 12 which includes an x-ray source 14 and a collimator 16. The x-ray source 14 produces an x-ray beam 18 which is collimated by the collimator 16. The collimator 16 may be one of several types of collimators generally used in x-ray studies of the peripherals. For example, the collimator 16 may be a block, a multi-leaf or a finger collimator. The collimated x-ray beam 20 passes through the area of interest 24 of the body 22 and strikes an x-ray image intensifier 26. In other embodiments of the x-ray imaging system, the collimator may be located immediately in front of the image intensifier. The image intensifier 26 processes the x-ray beam 20 so that it can be recorded by recording media 28 such as film or a CRT. The imaging device 12 is horizontally movable in the direction of arrow 30 so that x-ray images can be taken of the peripherals at a plurality of imaging stations. Leg studies may typically include 5-7 imaging stations. Horizontal movement of the imaging device 12 is provided by a stepper motor 32 which is controlled by a stepper controller 34. A contrast media injection device 36 is provided for injecting a contrast media into the patient 22 just prior to diagnostic imaging. Since x-ray diagnostic imaging is well known and commonly used, the details of these components need not be set forth herein any greater detail. The automatic collimation apparatus 38 interfaces with the collimator 16 of the system 10. The automatic collimation apparatus 38 performs a method in which an x-ray image of the peripherals is segmented into body parts and non-body parts. The method uses this information to provide the collimator 16 with appropriate collimator settings during image acquisition. The settings are used by the collimator 16 to adjust itself to cover as much of the non-body region and as little of the body region as possible, given the collimator's hardware constraints. The task of locating the body in an x-ray fluoroscopy image is difficult for a number of reasons. First, segmentation should take place without knowing which part of the body is being looked at. Second, since x-ray fluoroscopy studies use low radiation dosages, the images generally have low signal to noise ratios. Third, soft tissue boundaries often have very poor contrast. Due to the poor contrast makes, conventional edge detection algorithms fail to detect these boundaries. Fourth, existing collimation and noise make local intensity characteristics at a pixel inadequate for determining if it belongs to the body. Finally, segmentation and automatic collimation need to be done at image acquisition time. This places tight constraints on the complexity of image processing operators which can be used. The method performed by the automatic collimation apparatus 38 of the present invention successfully overcomes these difficulties. When the method is implemented as software, it operates robustly and efficiently on noisy, low contrast, possibly pre-collimated x-ray fluoroscopy images. In comparison with manual segmentation of body parts, the method has very high (&gt;95%) sensitivity and specificity. In one illustrative example of the method efficiently implemented as software, the method runs in less than 500 milliseconds or better per station, on a common 200 MHz Pentium Pro PC running Windows NT 4.0. With the use of parallelism and hardware acceleration, the running time can be further improved. FIG. 2 is a flow chart depicting the steps of the automatic collimation method of the present invention. The method segments the body regions in an x-ray image of the peripherals from the background (existing collimation, direct exposure). The information about where the body is in an image is used to provide settings for the collimator 16 of the x-ray imaging system 10 shown in FIG. 1. In step A of the block diagram of FIG. 2, the X-ray source of the x-ray imaging system is adjusted to provide an appropriate low-dosage fluoroscopy, and the collimator is adjusted to predetermined default settings for collimation. This step is repeated at each station. In step B, one of the incoming images at each station is preprocessed by downsizing and smoothing the image. These preprocessing steps are well known in the art and popularly used, thus further elaboration is not required here. Step C involves detecting soft-tissue boundaries using directional curvatures of intensity profiles. Finding the soft-tissue boundaries accurately is critical because the subsequent steps of global feature extraction and classification rely on this information. Prior art edge detection methods often fail to detect soft-tissue boundaries in x-ray images because the boundaries often have very low contrast and unusually defined intensity distributions. Soft-tissue boundaries are detected in the present invention by determining negative curvature points along line profiles of intensity in multiple chosen scanning directions. These provide a reliable indicator of low-contrast boundaries, such as soft-tissue. Even for very low contrast or hazy soft-tissue boundaries, well defined points of negative curvature exist on the line profiles of intensity. This is illustrated in FIG. 3A-3C. FIG. 3A depicts a peripheral x-ray image with low-contrast soft tissue boundaries near the ankle. An intensity profile along a horizontal line passing through the region of interest in FIG. 3A has points of negative curvature corresponding to these soft-tissue boundaries as shown in FIG, 3B. From a histogram equalized image of the line profile curvatures, very low-contrast boundaries are clearly preserved in the negative curvature image of FIG. 3C. Curvatures of line profiles of intensity are computed as follows with the following formulas: EQU Idiff(i,j)=I(i,j)-I(i,j-w))/wdenom(i,j)=w*sqrt(I+Idiff(i,j).sup.2) EQU hcurv(i,j)=(atan(Idiff(ij+w))-atan(Idiff(i,j)))(denom(i,j+w)+(denom(i,j)) I(i,j) is defined as the normalized intensity at pixel (i,j), w defines a parameter related to the window width and is dependent on the input image size, and atan is the are tangent function. The above computation is for horizontal curvatures of line profiles of intensity, however, similar computations can be performed for vertical or other directional curvatures of line profiles of intensity. In the present invention, all positive curvature values are ignored and therefore, removed by zeroing out the positive curvature values. It has been found that soft tissue boundaries are better captured by considering only negative curvatures. The curvature values shown in FIG. 3C are a combination of negative horizontal and vertical curvatures. The combined curvature value at a pixel is the minimum of the horizontal and vertical curvatures. Although the negative curvature image in FIG. 3C reliably indicates all soft-tissue boundaries, it still contains numerous spurious boundaries. Curvature is a second-order statistic and is therefore, noisy. In order for negative curvatures to be useful, the spurious boundary pixels must be reliably removed while preserving all the important boundaries of the soft-tissues. Accordingly, in step D of the block diagram of FIG. 2, the noise in the negative curvature image is adaptively removed using magnitude and alignment information. Magnitude information alone is not used because it may eliminate very faint soft-tissue boundaries which are desirable to locate. Noise is adaptively removed from the negative curvature image by strengthening well aligned curvature pixels and then finding an adaptive threshold based on the cumulative histogram of curvature values in the image. FIGS. 4A-4C depict adaptive noise removal from negative curvature images. FIG. 4A depicts an intensity image (X-ray image) and FIG. 4B depicts a corresponding noisy line profile negative curvature image. FIG. 4C depicts the negative curvature image of FIG. 4B after adaptively removing the noise. The images of FIGS. 4B and 4C are histogram equalized to show detail. Step E of the method depicted in FIG. 2 involves dividing the image into "regions". This is accomplished by providing the boundaries of significant regions in the image with a one-pixel representation of the cleaned up negative curvatures. The one-pixel representation is obtained by finding the local extrema in horizontal and vertical directions, combining them and performing simple noise removal using conventional connected components analysis techniques. The region information is for subsequently extracting global feature values which in turn, are used for classification as explained further on in greater detail. FIG. 5B shows the region boundaries for the image shown in FIG. 5A. In step F of the block diagram of FIG. 2, appropriate features such as range of intensity values, size, etc., are computed along horizontal and vertical lines within each region created in step E. The method of the invention preferably uses three features for segmentation. These features are homogeneity, representative intensity, and station number. Homogeneity is the minimum amount of intensity variation along chosen directions, per pixel, inside a region. Representative intensity is the median intensity in a region. Station number is number of the current station with respect to the full-leg study. Station numbers start at 0 at the pelvic region. Due to running time constraints, it is necessary that these features be simple. Additional features such as, the size of a region, the location of a region with respect to the station and with respect to the full-leg study, the variance of intensities in the region, etc., have been tried. However, supervised decision tree methods used for classification in the present invention, advantageously indicate which features are the most useful for discrimination. These and traditional feature selection methods have shown that no improvement in the classification results are obtained by adding more features to the above set of three. Step G of the method involves inter-region and intra-region propagation of the features. The features are first computed along scan lines in chosen directions in the image and then efficiently propagated over entire regions. Well known adaptive smoothing techniques are used herein for feature value propagation. FIGS. 6A-6C show the homogeneity and representative intensity feature values for a typical image after propagation. In step H of the block diagram of FIG. 2, each pixel in the image is classified as a body part or a non-body part, based on its feature values, using a decision tree. This involves constructing a set of rules which enable body and non-body regions to be determined on the basis of the feature values. The rules are constructed using supervised learning and are therefore, referred to herein as automatically learned classifiers. Automatically learned classifiers advantageously improve automatically over time, as new data comes in. Binary decision trees are used as the specific classifiers in the present invention. Binary decision trees are easy to understand and analyze and make classification very fast because if/else statements are used. Manually found and hard-coded rules typically used in prior art classification, are not used in the present invention because they may not generalize well, and the rules may need to be reconstructed when new data arrives. A predetermined number of data points (pixels in the image) are randomly selected as a training set. The training set is then used in a conventional decision tree method or algorithm to automatically construct the binary decision tree. The preferred decision tree method used is a conventional classification and regression tree (CART) method. This method is described by Breiman et al., Classification and Regression Trees, Chapman & Hall Publishers, 1984 (Software available from Salford Systems, Inc.). Since the CART method is well known, it need not be set forth in any great detail herein. However, some of the more important points of this method will now be described. The CART method takes as input a collection of labeled training instances, each instance having some attributes and a class label and produces a hierarchical decision tree as output. In the present invention, the instances are individual pixels, the attributes are the features computed above and the class labels are body part (1) and non-body part (0). CART then is used to construct binary decision trees from the data. At each stage, CART analyzes the training set to determine the test ("attribute?value.fwdarw.") that best discriminates between the classes, based on a feature evaluation criterion. The training set is then split into two subsets based on the best test. Tree growing continues recursively until no more nodes can be created. Once a full tree is constructed, CART prunes back the tree to remove noise-fitting nodes and/or marginally useful nodes, based on a portion of the training set that is reserved for this purpose. The preferred binary decision tree used in the present invention is relatively small and has only 160 terminal nodes. Having a small tree is important because it shows that the chosen features are appropriate for the classification task, and that the tree has a high probability of classifying unseen data correctly. If for example, the selected number of data points includes 95,000 data points, a tree with 95,000 terminal nodes can be theoretically built. A 160-node tree, which has high accuracy on hundreds of thousands of unseen data points, indicates that the features used are appropriate for classification. In using the binary tree for classification, the feature vectors at each pixel are individually "dropped down" the decision tree until a terminal node is reached. The label at the node is then assigned to the pixel. It should be understood that although the CART method is preferred, other decision tree methods or algorithms may be used if desired. In step I of the block diagram of FIG. 2, the classification result is post-processed to remove noise. This involves smoothing labels over regions and performing a connected components analysis. Such image processing operators are well known in the art and need no further description. In step J, a setting for the collimator is automatically determined from the classification result of step I. The collimator setting is selected to cover as much of the non-body part region as is possible while leaving uncovered as much of the body part as is possible. The collimator settings are automatically tailored to the constraints of the particular collimator used, for example, by taking into account the number of leafs, degrees of freedom, etc. In step k, the imaging system records the automatically computed collimator setting parameters which are subsequently used by the collimator. In step 1, the x-ray source is moved to the next station and the method is repeated until all the stations are processed and the collimation parameters are recorded by the imaging system. FIG. 7 depicts a graphical user interface which may be displayed by the automatic collimation apparatus. The images depicted are for a full leg study. It should be noted that other peripheral studies can be used as well. The interface displays multiple station input images 40 for the full leg, the input image of a single station 42, the segmentation result for the one station 44, and results for the full leg 46. It is understood that the above-described embodiments illustrate only a few of the many possible specific embodiments which can represent applications of the principles of the invention. For example, a human override option may be provided for allowing the physician or operator to override the automatically selected collimator setting if desired. This and other numerous modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the invention.