Patent Publication Number: US-2005123179-A1

Title: Method and system for automatic axial rotation correction in vivo images

Description:
FIELD OF THE INVENTION  
      The present invention relates generally to an endoscopic imaging system and, in particular, to axial rotation correction of in vivo images.  
     BACKGROUND OF THE INVENTION  
      Several in vivo measurement systems are known in the art. They include swallowed electronic capsules which collect data and which transmit the data to an external receiver system. These capsules, which are moved through the digestive system by the action of peristalsis, are used to measure pH (“Heidelberg” capsules), temperature (“CoreTemp” capsules) and pressure throughout the gastro-intestinal (GI) tract. They have also been used to measure gastric residence time, which is the time it takes for food to pass through the stomach and intestines. These capsules typically include a measuring system and a transmission system, wherein the measured data is transmitted at radio frequencies to a receiver system.  
      U.S. Pat. No. 5,604,531, assigned to the State of Israel, Ministry of Defense, Armament Development Authority, and incorporated herein by reference, teaches an in vivo measurement system, in particular an in vivo camera system, which is carried by a swallowed capsule. In addition to the camera system, there is an optical system for imaging an area of the GI tract onto the imager and a transmitter for transmitting the video output of the camera system. The capsule is equipped with a number of LEDs (light emitting diodes) as the lighting source for the imaging system. The overall system, including a capsule that can pass through the entire digestive tract, operates as an autonomous video endoscope. The electronic capsule images even the difficult to reach areas of the small intestine.  
      U.S. Pat. No. 6,632,175, assigned to Hewlett-Packard Development Company, L. P., and incorporated herein by reference, teaches a design of a swallowable data recorder medical device. The swallowable data recorder medical device includes a capsule having a sensing module for sensing a biological condition within a body. A recording module is provided including an atomic resolution storage device.  
      U.S. patent application No. 2003/0023150 A1, assigned to Olympus Optical Co., LTD., and incorporated herein by reference, teaches a design of a swallowed capsule-type medical device for conducting examination, therapy, or treatment, which travels through the inside of the somatic cavities and lumens of human beings or animals. Signals, including images captured by the capsule-type medical device, are transmitted to an external receiver and recorded on a recording unit. The images recorded are retrieved in a retrieving unit, displayed on the liquid crystal monitor and compared, by an endoscopic examination crew, with past endoscopic disease images that are stored in a disease imaging database.  
      One problem associated with the capsule imaging system is that when the capsule moves forward along the GI tract, there inevitably exists an axial rotation of the capsule around its own axis. This axial rotation causes inconsistent orientation of the captured images, which in turn causes diagnosis difficulties.  
      Hua Lee, et al. in their paper entitled “Image analysis, rectification and re-rendering in endoscopy surgery” (see http://www.ucop.edu/research/micro/abstracts/2k055.html), incorporated herein by reference, describes a video-endoscopy system used for assisting surgeons to perform minimal incision surgery. A scope assistant holds and positions the scope in response to the surgeon&#39;s verbal directions. The surgeon&#39;s visual feedback is provided by the scope and displayed on a monitor. The viewing configuration in endoscopy is ‘scope-centered&#39;. A large, on-axis rotation of the video scope and the camera will change the orientation of the body anatomy. The effect of that is the surgeon easily gets disoriented after repeated rotation of the scope view.  
      Note, Hua et al. teaches a method for a controllable endoscopic video system (controlled by an human assistant). The axial rotation of the video camera can be predicted and corrected. Furthermore, the axial rotation can be eliminated by using a robotic control system such as ROBDOC™ (see, http://www.robodoc.com/eng/index.html).  
      Other endoscopic video systems are uncontrollable systems. The camera is carried by a peristalsis propelled capsule. The axial rotation of the capsule is random, therefore, unpredictable.  
      There is a need therefore for an improved endoscopic imaging system that overcomes the problems set forth above.  
      These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings.  
     SUMMARY OF THE INVENTION  
      The need is met according to the present invention by providing a digital image processing method for automatic axial rotation correction for in vivo images that includes selecting, as a reference image, a first arbitrary in vivo image from a plurality of in vivo images, and subsequently, finding a rotation angle between a second arbitrary in vivo image selected from the plurality of in vivo images and the reference image. The method next corrects the orientation of the second arbitrary in vivo image, with respect to orientation of the reference image and corresponding to the rotation angle, before finding the rotation angle between other selected in vivo images and the reference image. Additionally, the method corrects for the other selected in vivo images that do not match the reference image&#39;s orientation and where there exists a rotation angle between the other selected in vivo images and the reference image. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a prior art block diagram illustration of an in vivo camera system;  
       FIG. 2  is an exemplary illustration of the concept of an examination bundle according to the present invention;  
       FIG. 2A  is an exemplary illustration of the concept of an examination bundlette according to the present invention;  
       FIG. 3A  is a flowchart illustrating information flow for a real-time abnormality detection method;  
       FIG. 3B  is a flowchart illustrating information flow of the in vivo image with axial rotation correction of the present invention;  
       FIG. 4  is a schematic diagram of an exemplary examination bundlette processing hardware system useful in practicing the present invention;  
       FIG. 5  is a flowchart illustrating the in vivo image axial rotation correction method according to the present invention;  
       FIG. 6A  is a graph showing an in vivo imaging system capsule in a GI tract;  
       FIG. 6B  is a graph illustrating three-dimensional coordinate systems of the in vivo imaging system at three locations in a GI tract;  
       FIG. 6C  is a graph illustrating an in vivo image plane and its two-dimensional coordinate system;  
       FIG. 6D  illustrates an in vivo image with an object and another in vivo image with an rotated object;  
       FIG. 7  is a graph illustrating an optic flow image;  
       FIG. 8A  illustrates an optic flow image simulating a camera moving forward along its optical axis while rotating around its optical axis;  
       FIG. 8B  illustrates an optic flow image simulating a camera moving rotating around its optical axis, and  
       FIG. 8C  illustrates an optic flow image simulating a camera rotating around its optical axis. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
      In the following description, various aspects of the present invention will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the present invention. However, it will also be apparent to one skilled in the art that the present invention may be practiced without the specific details presented herein. Furthermore, well-known features may be omitted or simplified in order not to obscure the present invention.  
      During a typical examination of a body lumen, the in vivo camera system captures a large number of images. The images can be analyzed individually, or sequentially, as frames of a video sequence. An individual image or frame without context has limited value. Some contextual information is frequently available prior to or during the image collection process; other contextual information can be gathered or generated as the images are processed after data collection. Any contextual information will be referred to as metadata.  
      Metadata is analogous to the image header data that accompanies many digital image files.  
       FIG. 1  shows a block diagram of the in vivo video camera system described in U.S. Pat. No. 5,604,531. The system captures and transmits images of the gastro-intestinal (GI) tract while passing through the gastro-intestinal lumen. The system contains a storage unit  100 , a data processor  102 , a camera  104 , an image transmitter  106 , an image receiver  108 , which usually includes an antenna array (not shown herein), and an image monitor  110 . Storage unit  100 , data processor  102 , image monitor  110 , and image receiver  108  are located outside the patient&#39;s body. Camera  104 , as it transits the GI tract, is in communication with image transmitter  106  located in capsule  112  and image receiver  108  located outside the body. Data processor  102  transfers frame data to and from storage unit  100  while the former analyzes the data. Data processor  102  also transmits the analyzed data to image monitor  110  where a physician views it. The data can be viewed in real time or at some later date.  
      Referring to  FIG. 2 , the complete set of all images captured during the examination, along with any corresponding metadata, will be referred to as an examination bundle  200 . The examination bundle  200  consists of a collection of image packets  202  and a section containing general metadata  204 .  
      An image packet  206  comprises two sections: the pixel data  208  of an image that has been captured by the in vivo camera system, and image specific metadata  210 . The image specific metadata  210  can be further refined into image specific collection data  212 , image specific physical data  214  and inferred image specific data  216 . Image specific collection data  212  contains information such as the frame index number, frame capture rate, frame capture time, and frame exposure level. Image specific physical data  214  contains information such as the relative position of the capsule when the image was captured, the distance traveled from the position of initial image capture, the instantaneous velocity of the capsule, capsule orientation, and non-image sensed characteristics such as pH, pressure, temperature, and impedance. Inferred image specific data  216  includes location and description of detected abnormalities within the image, and any pathologies that have been identified. This data can be obtained either from a physician or by automated methods.  
      The general metadata  204  contains such information as the date of the examination, the patient identification, the name or identification of the referring physician, the purpose of the examination, suspected abnormalities and/or detection, and any information pertinent to the examination bundle  200 . It can also include general image information such as image storage format (e.g., GIF, TIFF or JPEG-based), number of lines, and number of pixels per line.  
      Referring to  FIG. 2A , the image packet  206  and the general metadata  204  are combined to form an examination bundlette  220  suitable for real-time abnormality detection.  
      It will be understood and appreciated that the order and specific contents of the general metadata or image specific metadata may vary without changing the functionality of the examination bundle.  
      Referring now to  FIG. 3A  and specific components shown in  FIG. 2 , an exemplary application of the capsule in vivo imaging system is described.  FIG. 3A  is a flowchart illustrating a real-time automatic abnormality detection method. In  FIG. 3A , an in vivo imaging system  300  can be realized by using systems such as the swallowed capsule described in U.S. Pat. No. 5,604,531. An in vivo image  208  (as shown in  FIG. 2 ) is captured in an in vivo image acquisition step  302 . In a step of In Vivo Examination Bundlette Formation  304 , the image  208  is combined with image specific data  210  to form an image packet  206 . The image packet  206  is further combined with general metadata  204  and compressed to become an examination bundlette  220 . The examination bundlette  220  is transmitted to a proximal in vitro computing device through radio frequency in a step of RF transmission  306 . An in vitro computing device  320  is either a portable computer system attached to a belt worn by the patient or in near proximity. Alternatively, it is a system such as shown in  FIG. 4  and will be described in detail later. The transmitted examination bundlette  220  is received in the proximal in vitro computing device  320  by an In Vivo RF Receiver  308 .  
      Data received in the in vitro computing device  320  is examined for any sign of disease in Abnormality detection operation  310 . Details of the step of abnormality detection can be found in commonly assigned, co-pending U.S. patent application Ser. No. (our docket 86558), entitled “METHOD AND SYSTEM FOR REAL-TIME AUTOMATIC ABNORMALITY DETECTION OF IN VIVO IMAGES”, and which is incorporated herein by reference.  
       FIG. 3B  shows a diagram of information flow of the present invention. To ensure effective detection and diagnosis of an abnormality, images from RF Receiver  308  are adjusted in a step of Image axial rotation correction  309  before the abnormality detection operation  310  takes place (see  FIG. 3B ).  
      The step of Image axial rotation correction  309  is specifically detailed in  FIG. 5 . Any alarm signals from step  310  will be sent to a local site  314  and to a remote health care site  316  through communication connection  312 . An exemplary communication connection  312  could be a broadband network connected to the in vitro computing system  320 . The connection from the broadband network to the in vitro computing system  320  could be either a wired connection or a wireless connection. Again, the in vitro computing device  320  could be a portable computer system attached to a belt worn by the patient.  
      A plurality of images  501  received from RF receiver  308  are input to operation  502  of “Getting two images” (a first arbitrary image and a second arbitrary image) I n  and I n+δ , where n is an index of an image sequence, δ is an index offset. An exemplary value for δ is 1. The in vivo camera is carried by a peristalsis propelled capsule. Axial rotation of the capsule causes the image plane to rotate about its optical axis. Exemplary images in step  502  are shown in  FIG. 6B . For clarity, detailed description of the remaining operational steps ( 503 ,  504 ,  505 ,  506 ,  507 ,  508 ,  509 ,  510 ,  514 ,  516 ,  518 , and  520 ) of  FIG. 5  are discussed in a later section, once the angular relationship between successive image planes is explained.  
      Along a GI tract  606 , there are images (planes) I n−δ  ( 608 ), in ( 610 ) and I n+δ  ( 612 ) at GI positions p n−δ  ( 607 ), p n  ( 609 ) and p n+δ  ( 611 ) respectively. There are three-dimensional coordinate systems, S n−δ  ( 614 ), S n  ( 616 ) and S n+δ  ( 618 ) attached to images I n−δ , I n  and I n+δ  accordingly.  
      The X and Y axes of the three-dimensional systems S n−δ  ( 614 ), S n  ( 616 ) and S n+δ  ( 618 ) are aligned with the V and U axes of a two-dimensional coordinate system of the corresponding images (planes) I n−δ  ( 608 ), I n  ( 610 ) and I +δ  ( 612 ). An exemplary two-dimensional coordinate system ( 620 ) of an image with the U and V axes is shown in  FIG. 6C . Note that the origin of the two-dimensional coordinate system is at the center of the image plane. The Z axes of the three-dimensional systems S n−δ  ( 614 ), S n  ( 616 ) and S n+δ  ( 618 ) are perpendicular to their corresponding image planes I n−δ  ( 608 ), I n  ( 610 ) and I n+δ  ( 612 ). The Z axes of the three-dimensional systems S n−δ  ( 614 ), S n  ( 616 ) and S n+δ  ( 618 ) are aligned with optical axes of the in vivo camera at the corresponding positions where images I n−δ  ( 608 ), I n  ( 610 ) and I n+δ  ( 612 ) are captured. When the camera rotates around its optical axis, the three-dimensional system attached to the camera image plane also rotates around its Z axis. The rotation angle is defined respective to a right-hand system or a left-hand system as is known to ordinary people skilled in the art. This rotation makes fixed objects (the inner walls of the GI tract) in the three-dimensional space rotate in an opposite direction in the rotated three-dimensional coordinate system. This phenomenon is illustrated in  FIG. 6D . An object  630  is projected onto image plane I n  ( 610 ) at position p n  ( 609 ). Object  630  has four corner points  632 ,  634 ,  636  and  638 . When the in vivo camera advances to position p n+δ  ( 611 ) there is a counterclockwise rotation θ n+δ  ( 615 ) around the Z axis associated with the camera forward motion. The object in image I n+δ  ( 612 ) captured at position p n+δ  ( 611 ) appears to rotate clockwise with −θ n+δ  degrees in addition to a magnification effect due to the camera forward motion. Object  631  has four corner points  633 ,  635 ,  637  and  639 . If image plane I n  ( 610 ) is taken as a reference plane, the four points ( 633 ,  635 ,  637  and  639 ) in image plane I n+δ  ( 612 ) appear to move away from their original positions (points  632 ,  634 ,  636  and  638 ) in the reference image plane. This motion of points in the image plane can be described using a common terminology, ‘optic flow’ which is widely adopted in the computer vision community.  
       FIG. 7  illustrate the optic flow image  710  of object  630  in image  610  (shown in  FIG. 6D ). Arrows  732 ,  734 ,  736  and  738  indicate the motion direction of points  632 ,  634 ,  636  and  638  to points  633 ,  635 ,  637  and  639  of object  631  in a reference plane.  
      The method of the present invention is to determine the rotation angle θ, in general, between consecutive image coordinate systems (angle between the V axes or between U axes of two images) in order to perform rotation correction. This task is accomplished first by finding corresponding point pairs in consecutive images in a step of Corresponding point pair searching  504 . Exemplary corresponding point pairs are  632 - 633 ,  634 - 635 ,  636 - 637 , and  638 - 639  (as shown in  FIG. 6D ). There are abundantly well known algorithms to fulfill this corresponding point pair searching task. For example, a phased-based image motion estimation method that is not sensitive to low-pass variations in image intensity where shadows and illumination vary (see “Phase-based Image Motion Estimation and Registration,” by Magnus Hemmendorff, Mats T. Andersson, and Hans Knutsson,  
      http://www.telecom.tuc.gr/paperdb/icassp99/PDF/AUTHOR/IC991287.PDF).  
      The estimation of angle between two consecutive images is performed in step  506  (shown in  FIG. 5 ) of Rotation angle estimation. In general, this estimation can be realized by using algorithms such as 2D-2D absolute orientation detection (see “Computer and Robot Vision,” by Robert M. Haralick and Linda G. Shapiro) as an exemplary scheme.  
      Once again, referring to  FIG. 6D , Using image planes I n  ( 610 ) and I n+δ  ( 612 ) as exemplary images, denote T 2D coordinate points from I n  ( 610 ) by I 1   n , . . . , p T   n  (for example, points  632 ,  634 ,  636  and  638 , and here T=4). These could correspond to the points in I +δ  ( 612 ) denoted by p 1   n+δ , . . . , p T   n+δ  (for example, points  633 ,  635 ,  637  and  639 ). Note, this correspondence has been accomplished in step  504  (shown in  FIG. 5 ) of Corresponding point pair searching. This 2D orientation detection attempts to determine from the corresponding point pairs (for example, pairs  632 - 633 ,  634 - 635 ,  636 - 637 , and  638 - 639 ) a more precise estimate of a rotation matrix R and a translation d such that p t   n+δ =Rp t   n +d,t=1, . . . , T. Since errors are likely embedded in step of Corresponding point pair searching  504 , the real problem becomes a minimization problem. Determine R and d such that the weighted sum of the residual errors ε 2  is minimized:  
               ɛ   2     =       ∑     t   =   1     T     ⁢       w   t     ⁢              p   t     n   +   δ       -     (       Rp   t   n     +   d     )            2                 (   1   )             
 
 The weights w t ≧0 and Σ t=1   T w t =1. Exemplary value of the weights could be w t =1/T. 
 
      First, taking the partial derivative of Equation (1) with respective to the translation d and setting the partial derivative to 0 yields 
 
 d={overscore (p)}   n+δ   −R{overscore (p)}   n    (2) 
 
 where {overscore (p)} n+δ =Σ t=1   T w t p t   n+δ  and {overscore (p)} n =Σ t=1   T w t p t   n . Applying Equation (2) in Equation (1) results in  
               ɛ   2     =       ∑     t   =   1     T     ⁢       w   t     ⁡     [           (       p   t     n   +   δ       -       p   _       n   +   δ         )     ′     ⁢     (       p   t     n   +   δ       -       p   _       n   +   δ         )       -     2   ⁢     (       p   t     n   +   δ       -       p   _       n   +   δ         )     ⁢     R   ⁡     (       p   t   n     -       p   _     n       )         +         (       p   t   n     -       p   _     n       )     ′     ⁢     (       p   t   n     -       p   _     n       )         ]                 (   3   )             
 
 Notice the fact that  
             R   =     [           cos   ⁡     (     θ     n   +   δ       )               -     sin   ⁡     (     θ     n   +   δ       )         ⁢                   sin   ⁡     (     θ     n   +   δ       )               cos   ⁡     (     θ     n   +   δ       )       ⁢               ]             (   4   )             
 
 Notice also that every point such as  632 ,  634 ,  636 ,  638 ,  633 ,  635 ,  637  or  639  in the image plane is represented by a two-dimensional vector in the U-V coordinate system as shown in  FIG. 6C . Therefore, p t   n  and p t   n+δ  can be expressed as  
                 p   t   n     =         (           p     u   ,   t     n               p     v   ,   t     n           )     ⁢           ⁢   and   ⁢           ⁢     p   t     n   +   δ         =     (           p     u   ,   t       n   +   δ                 p     v   ,   t       n   +   δ             )         ⁢     
     ⁢   and           (   5   )                   p   _     n     =         (             p   _     u   n                 p   _     v   n           )     ⁢           ⁢   and   ⁢           ⁢       p   _       n   +   δ         =     (             p   _     u     n   +   δ                   p   _     v     n   +   δ             )               (   6   )             
 
 Applying Equations (4), (5) and (6) to Equation (3) and setting to zero the partial derivative of ε 2  with respect to θ n+δ  results in 0=A sin(θ n+δ )+B cos(θ n+δ )  
     where     
       A   =       ∑     t   =   1     T     ⁢       w   t     ⁡     [         (       p     u   ,   t       n   +   δ       -       p   _     u     n   +   δ         )     ⁢     (       p     u   ,   t     n     -       p   _     u   n       )       +       (       p     v   ,   t       n   +   δ       -       p   _     v     n   +   δ         )     ⁢     (       p     v   ,   t     n     -       p   _     v   n       )         ]             
     and     
       B   =       ∑     t   =   1     T     ⁢         w   t     ⁡     [         (       p     u   ,   t       n   +   δ       -       p   _     u     n   +   δ         )     ⁢     (       p     u   ,   t     n     -       p   _     u   n       )       +       (       p     v   ,   t       n   +   δ       -       p   _     v     n   +   δ         )     ⁢     (       p     v   ,   t     n     -       p   _     v   n       )         ]       .           
 
 The absolute value of the rotation angle θ n+δ  can be computed as 
 
|θ n+δ |=cos −1 ( A/{square root}{square root over (A     2     +B     2     )})    (7) 
 
 After finding the absolute value of the rotation angle (for example, θ n+δ ) between two consecutive image planes (for example, planes I n  ( 610 ) and I n+δ  ( 612 )), the next step is to find the rotation direction, or the sign of the rotation angle in a step of Rotation angle sign detection  508 . The operation of rotation angle sign detection  508  is explained by using a computer-driven simulated case. 
 
       FIG. 8  displays the computer simulated optic flow of a set of 2D points (fourteen points) in two consecutive image planes, for example, planes I n  ( 610 ) and I n+δ  ( 612 ) (shown in  FIG. 6B ). These fourteen points are the perspective projections of fourteen non-coplanar points in the three-dimensional space. The focal length of the simulated camera is one unit (exemplary unit is inch). Image plane I n  ( 610 ) is used as a reference plane. With respect to image plane I n  ( 610 ), image plane I n+δ  ( 612 ) (in fact, the camera) rotates an exemplary 18 degrees clockwise around its optical axis that is aligned with the Z-axis of the three-dimensional coordinate system. Image plane I n+δ  ( 612 ) (in fact, the camera) also moves forward along its optical axis, or the Z-axis of the three-dimensional coordinate system, by an exemplary distance of 0.5 units (exemplary unit is inch) toward the cloud of fourteen non-coplanar points in the three-dimensional space. Arrows such as  806  in graph  802  of  FIG. 8A  illustrate the optic flow of imaged points such as  804  moving from their positions in image plane I n  ( 610 ) to their positions in image plane I n+δ  ( 612 ).  
      Recall that the simulated motion includes translation along the Z-axis (moving forward) and rotation around the Z-axis. Hence, arrows such as  806  can be decomposed into two components: a translational component and a rotational component. Graph  812  in  FIG. 8B  illustrates the rotational component of optic flow in  FIG. 8A . Arrow  816  is the rotational component of arrow  806  for point  804  (shown in  FIG. 8A ) due to the rotation of the camera. Graph  822  in  FIG. 8C  illustrates the translational component of optic flow in  FIG. 8A . Arrow  826  is the translational component of arrow  806  for point  804  (shown in  FIG. 8A ) due to the forward motion of the camera. Notice that image point  804  is a projection of a three-dimensional point on the X axis in the three-dimensional space. If the camera has only translation motion along its optical axis or the Z-axis of the three-dimensional coordinate system, the new position of point  804  resides on the V axis of the image plane (see exemplary arrow  826  in graph  822 ). This rule applies to other points on the V axis. Likewise, if the camera has only translation motion along its optical axis or the Z-axis of the three-dimensional coordinate system, the new position of a point on U axis resides on the U axis of the new image plane. In general, if the camera has only translation motion along its optical axis or the Z-axis of the three-dimensional coordinate system, the new position of a point anywhere in the image plane is on a line passing through the point and the origin. Now returning back to  FIG. 8A , optic flow arrow  806  for point  804  pointing to negative U direction reveals that there exists a rotational component of the optic flow pointing to the negative U direction as well, just as arrow  816  shown in  FIG. 8B . A rotational component of the optic flow pointing to the negative U direction indicates that the camera rotates clockwise. On the other hand, a rotational component of the optic flow pointing to the positive U direction indicates that the camera rotates counterclockwise. Therefore, by evaluating the optic flow of points on the V axis, the direction (of the sign) of the rotation angle of the camera can be determined. People skilled in the art can easily extend this analysis to points that are not on the V axis. As for the simulated case, using Equations (1) through (7) and the sign detection method stated above, the rotation angle is computed as 17.9 degrees clockwise from the coordinate system of image plane I n  ( 610 ) to the coordinate system of image plane I n+δ  ( 612 ) (both are shown in  FIG. 6D ).  
      Referring again to  FIG. 5 , there is a step of Rotation angle accumulation  514 . For a sequence of in vivo images, the user could select any one image among the available images as the reference image and apply axial rotation correction to all the other images. The corrected images are not necessarily consecutive images of the reference image. For example, if image I −δ  is selected as the reference image, then image I n+δ  has to be rotated by an angle θ n+δ  so that image I n+δ  will have the same orientation as image I n−δ . Image points matching algorithms such as optic flow computation performs best when they are applied to two images with extensive overlaps (regions having the same objects). Obviously, image I n+δ  has more overlaps with image I n  than with image I n−δ . So the real rotation angle θ n+δ  for orientation correction, if I n−δ  is selected as the reference image, is the accumulated rotation angle from I n−δ  to I n+δ  computed using Equations (1) through (7) and the sign detection method stated above. In step  516  of Orientation correction, compute  
             I   ^       n   +   δ       =       R   ^     ⁢           ⁢     I     n   +   δ           ,       where   ⁢           ⁢     R   ^       =       [           cos   ⁡     (     -     θ     n   +   δ         )               -     sin   ⁡     (     -     θ     n   +   δ         )         ⁢                   sin   ⁡     (     -     θ     n   +   δ         )               cos   ⁡     (     -     θ     n   +   δ         )       ⁢               ]     .           
 
 orientation as I −δ , if I −δ  is selected as the reference image. 
 
      The flow chart in  FIG. 5  is an example embodiment of the present invention, where the axial rotation correction starts from I 0 . That is, n is initialized as zero. Set δ to one. Use I 0  as the reference image, find the rotation angle and the direction of the angle for I 1  using operations  504 ,  506 ,  508  and  514 . After I 1  is axial rotation corrected, an operation query  518  is performed to see if all images are processed. If so, the algorithm goes to ending operation  520 , otherwise, the algorithm increases n by δ, then gets I 2 . Use the original I 1  (before axial rotation correction) to find the angle between I 1  and I 2 . The process continues until all the images are corrected.  
      The axial rotation correction has been formulated in terms of optic flow technology. People skilled in the art should be able to formulate the problem using other technologies such as motion analysis, image correspondence analysis and so on. The axial rotation correction can be realized in real-time or offline.  
       FIG. 4  shows an exemplary of an examination bundlette processing, including the axial rotation correction hardware system useful in practicing the present invention that includes a template source  400  and an RF receiver  412 . The template from the template source  400  is provided to an examination bundlette processor  402 , such as a personal computer, or a work station such as a Sun Sparc™ workstation. The RF receiver  412  passes the examination bundlette to the examination bundlette processor  402 . The examination bundlette processor  402  preferably is connected to a CRT display  404 , an operator interface, such as a keyboard  406  and/or a mouse  408 . Examination bundlette processor  402  is also connected to computer readable storage medium  407 . The examination bundlette processor  402  transmits processed and adjusted digital images including axial rotation correction and metadata to an output device  409 . Output device  409  can comprise a hard copy printer, a long-term image storage device, or another processor networked together. The examination bundlette processor  402  is also linked to a communication link  414  or a telecommunication device connected, for example, to a broadband network.  
      The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention.  
     Parts List  
     
         
           100  Storage Unit  
           102  Data Processor  
           104  Camera  
           106  Image Transmitter  
           108  Image Receiver  
           110  Image Monitor  
           112  Capsule  
           200  Examination Bundle  
           202  Image Packets  
           204  General Metadata  
           206  Image Packet  
           208  Pixel Data  
           210  Image Specific Metadata  
           212  Image Specific Collection Data  
           214  Image Specific Physical Data  
           216  Inferred Image Specific Data  
           220  Examination Bundlette  
           300  In Vivo Imaging system  
           302  In Vivo Image Acquisition  
           304  Forming Examination Bundlette  
           306  RF Transmission  
           306  Examination Bundlette Storing  
           308  RF Receiver  
           309  Image axial rotation correction  
           310  Abnormality Detection  
           312  Communication Connection  
           314 Local Site  
           316  Remote Site  
           320  In Vitro Computing Device  
           400  Template source  
           402  Examination Bundlette processor  
           404  Image display  
           406  Data and command entry device  
           407  Computer readable storage medium  
           408  Data and command control device  
           409  Output device  
           412  RF transmission  
           414  Communication link  
           501  images  
           502  Getting two images  
           503  image  
           504  Corresponding point pair searching  
           505  image  
           506  Rotation angle estimation  
           507  angle  
           510  Rotation angle sign detection  
           514  angle  
           510  a step  
           508  Rotation angle accumulation  
           510  Orientation correction  
           518  All images done? 
           520  end  
           602  GI tract  
           604  capsule  
           606  GI tract Trajectory  
           607  position point  
           608  image plane  
           609  position point  
           610  image plane  
           611  position point  
           612  image plane  
           614  coordinate system  
           615  an angle  
           616  coordinate system  
           618  coordinate system  
           620  two-dimensional coordinate system  
           630  an image object  
           631  an image object  
           632  an image point  
           633  an image point  
           634  an image point  
           635  an image point  
           636  an image point  
           637  an image point  
           638  an image point  
           639  an image point  
           710  an optic flow image  
           732  an arrow  
           734  an arrow  
           736  an arrow  
           738  an arrow  
           802  a simulated camera motion optic flow image  
           804  an image point  
           806  an arrow  
           812  a simulated camera motion optic flow image  
           816  an arrow  
           822  a simulated camera motion optic flow image  
           626  an arrow