Abstract:
The invention is generally directed to imaging systems, and more particularly to systems and methods for pattern recognition. In one embodiment, a medical imaging system includes an imaging device and a computer-usable medium, electrically coupled to the imaging device, having a sequence of instructions which, when executed by a processor, causes said processor to execute a process including generating an image from signals received by the imaging device, deconvolving the image, and then extracting a desired pattern from the deconvolved image.

Description:
FIELD OF THE INVENTION 
   The field of the invention relates to imaging systems, and more particularly to systems and methods for pattern recognition. 
   BACKGROUND OF THE INVENTION 
   A fundamental step in image interpretation is pattern recognition, which essentially involves the process of analyzing one or more pixels of a given image and assigning one or more pixels to one of a limited number of pre-defined categories, or classes, based on the value(s) of the one or more pixels. One or more of the pre-defined categories are the patterns, or features, to be recognized and extracted. As is known in the art, the algorithm to determine which category to assign a pixel of an image may be established by providing a generic computational procedure a large number of sample images for each category and having the computational procedure determine the characteristics for each category that are unique compared to the other categories, such as color or brightness. 
   The accuracy of this approach is dependent upon the effectiveness of the determined unique characteristics. For example, turning to  FIG. 1   a , an image is shown having a generally circular region  10  of gray points in the center of the image. In one pattern recognition system, it may be desirable to identify and locate this circular region  10  in the image. To develop such a system, small regions of pixels are evaluated throughout the picture. By evaluating the values and/or patterns of certain characteristics, such as brightness or color, of each pixel, or regions of pixels, and mapping or graphing the values, unique characteristics may become apparent. For example, turning to  FIG. 1   d , the brightness of each region of pixels is evaluated, and a mean value of brightness for each region of pixels is calculated along with a corresponding standard deviation and graphed according to its mean and standard deviation. From such a graphing, two groups become apparent, regions of pixels  14  associated with areas of the image within the circular region  10  and regions of pixels  16  associated with areas of the image outside the circular region  10 . From this information, pre-defined categories may be established, and the pattern recognition algorithm may be configured to evaluate regions of pixels, assign them to the appropriate categories, and extract the desired patterns or features. 
   However, often times, imaging systems may introduce imperfections, such as blurring, into the images they produce, and thus, may generate images such as that shown in  FIG. 1   b  instead of that shown in  FIG. 1   a . The desired pattern, shown in the circular region  10  of  FIG. 1   a , cannot be visually detected in  FIG. 1   b . A pattern recognition system that can detect a desired pattern from such an image would be desirable. 
   SUMMARY OF THE INVENTION 
   The invention is generally directed to imaging systems, and more particularly to systems and methods for pattern recognition. In one embodiment, a medical imaging system includes an imaging device and a computer-usable medium, electrically coupled to the imaging device, having a sequence of instructions which, when executed by a processor, causes said processor to execute a process including generating an image from signals received by the imaging device, deconvolving the image, and then extracting a desired pattern from the deconvolved image. 
   In another embodiment, a process for pattern recognition includes the steps of generating an image, deconvolving the image, and then extracting a desired pattern from the deconvolved image. 
   Other systems, methods, features and advantages of the invention will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     In order to better appreciate how the above-recited and other advantages and objects of the inventions are obtained, a more particular description of the embodiments briefly described above will be rendered by reference to specific embodiments thereof, which are illustrated in the accompanying drawings. It should be noted that the components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like reference numerals designate corresponding parts throughout the different views. However, like parts do not always have like reference numerals. Moreover, all illustrations are intended to convey concepts, where relative sizes, shapes and other detailed attributes may be illustrated schematically rather than literally or precisely. 
       FIG. 1   a  is an image having a plurality of patterns or features to be extracted; 
       FIG. 1   b  is the image of  FIG. 1   a  with blurring introduced into the image; 
       FIG. 1   c  shows an image with blurring; 
       FIG. 1   d  is an image of  FIG. 1   c  after a deconvolution algorithm as been applied; 
       FIG. 1   e  is a graph of a plurality of regions of pixels shown in  FIG. 1   a;    
       FIG. 2  is a diagram of a basic block diagram of a preferred embodiment of the invention; and 
       FIG. 3  is a diagram of a preferred embodiment of the invention. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   Described below is a new pattern recognition method and system that extracts patterns or features from an image generated by an imaging system  20  comprising an imaging device  22  and a processor  24 , as shown in  FIG. 2 . The imaging system  20  may be a medical imaging system and the imaging device  22  may be an ultrasound transducer or an apparatus for obtaining images using a light source, such as through optical coherence tomography (OCT). Image acquisition using OCT is described in Huang et al., “Optical Coherence Tomography,” Science, 254, Nov. 22, 1991, pp 1178-1181, which is incorporated herein by reference. A type of OCT imaging device, called an optical coherence domain reflectometer (OCDR) is disclosed in Swanson U.S. Pat. No. 5,321,501, which is incorporated herein by reference. The OCDR is capable of electronically performing two- and three-dimensional image scans over an extended longitudinal or depth range with sharp focus and high resolution and sensitivity over the range. 
   As mentioned above, an imaging system  20  may introduce imperfections, such as blurring, into a generated image, as shown in  FIG. 1   c . One common approach to remove the imperfection is to computationally reverse the imperfection in the generated image. This is particularly effective when the imperfection is predictable or known. This approach is known in the art as deconvolution. In one method known in the art to create a deconvolution algorithm, an additional image of a single bright point source, such as a dot, is generated by the imaging system  20 . When the imperfection is present in the image, an algorithm is created that reverses the blurred image to recreate the actual image with better precision. 
   Once this deconvolution algorithm is created, it may applied to all images created by the imaging system  20 . To deconvolve such images, each image is represented as a plurality of points, preferably infinitesimal points, and the algorithm is applied to each individual point. One of ordinary skill in the art can appreciate that such an algorithm is effective only for limited types of imperfections, such as those created by a linear shifting variant system. There are many types of imperfections that may remain unaffected by deconvolution. Thus, as an example, for the image shown in  FIG. 1   c , a typical deconvolution system will produce the image shown in  FIG. 1   d , which shows slight improvement but still lacks the quality of the image shown in  FIG. 1   a . For instance, the desired pattern in the circular region  10  still cannot be visually detected in  FIG. 1   d . Such images are still disregarded as unhelpful. 
   However, even though the image in  FIG. 1   d  does not provide any visual help, there is still useful information that may be obtained from the deconvolution process. Turning back to  FIG. 1   e , small regions of pixels may be evaluated throughout the image in  FIG. 1   d . By evaluating the values and/or patterns of certain characteristics, such as brightness or color, of each pixel, or regions of pixels, and mapping or graphing the values, unique characteristics may still become apparent from the graph, even though they may not be visually apparent. For example, turning to  FIG. 1   e , the brightness of each region of pixels is evaluated, and a mean value of brightness for each region of pixels is calculated along with a corresponding standard deviation and graphed according to its mean and standard deviation. From such a graphing, two groups may become apparent, regions of pixels  14  associated with areas of the image within the circular region  10  and regions of pixels  16  associated with areas of the image outside the circular region  10 . From this information, pre-defined categories may be established, and the pattern recognition algorithm may still be effective in extracting the desired patterns or features. 
   In other words, the deconvolution of an image may function as a contrast enhancer, which causes a better separation between categories. Accordingly, pattern recognition applied to such a deconvolved image may generate more accurate results. 
   Turning to  FIG. 3 , an example embodiment of a new pattern recognition method is shown as applied to an image generated by a processor  24  of an imaging system  20  based on data received by an imaging device  22 , such as a medical imaging device, electrically coupled to the processor  24 . After the image is generated (step  100 ), particular regions of interest may be selected and segmented for further analysis (step  200 ). Subsequently, the segmented image may be deconvolved (step  300 ), using any known deconvolution method. After the deconvolution (step  300 ), the pixels, or regions of pixels, of the image may be assigned to pre-defined categories, and then the desired feature(s) may be extracted (step  400 ) and further evaluated in search for a desired pattern (step  500 ). 
   In the foregoing specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. For example, the reader is to understand that the specific ordering and combination of process actions described herein is merely illustrative, and the invention may appropriately be performed using different or additional process actions, or a different combination or ordering of process actions. For example, this invention is particularly suited for applications involving medical imaging devices, but can be used on any design involving imaging devices in general. As a further example, each feature of one embodiment can be mixed and matched with other features shown in other embodiments. Additionally and obviously, features may be added or subtracted as desired. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.