Patent Publication Number: US-7715605-B2

Title: Systems and methods for computer aided detection of spinal curvature using images and angle measurements

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATION 
     This application claims the benefit of U.S. Provisional Application Ser. No. 60/714,591, filed Sep. 7, 2005 and entitled “Detection of the Curvature of the Spine from X-Ray Images and Angle Measurements”, the content of which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The present disclosure relates to systems and methods for providing automated detection of spinal curvature and, more particularly, to systems and methods for providing automated detection of spinal curvature using images of the spine and angle measurements. 
     2. Discussion of Related Art 
     The spine is made up of twenty-four vertebrae which are separated by discs. The normal curves of the spine provide the spine with the properties of flexibility, resiliency and shock absorbency. Scoliosis is a musculoskeletal condition in which there is an abnormal lateral curvature of the spine, causing the spinal column to bend to the left or right. Various studies have defined scoliosis as a lateral deviation of the normal vertical line of the spine which, when measured by X-rays, is greater than ten degrees. Whereas a normal spine, when the body is viewed from directly behind, has the appearance of a straight line, a spine with scoliosis resembles the letter S or C, because of the abnormal curvature. Scoliosis starts when the spine does not develop its normal front to back arches, which causes unusual weight to be carried on the discs. The center of certain discs shifts to one side and the vertebra tip to the other. This misalignment, called a subluxation, causes the spine to bend to the left or right. To compensate for this bend, the spine tips to the other side at another level and the result is scoliosis. 
     Scoliosis can occur at any age, but most often appears in early adolescence. Screening is useful when early identification enables treatment to be started that may halt the progression of the deformity. The Scoliosis Research Society and the American Academy of Orthopaedic Surgeons have endorsed school screening programs to detect scoliosis curves before they may become advanced. To diagnose the condition, a doctor may request an X-ray to get a better view of the spine. In an X-ray image, the curve of the scoliosis is usually measured by looking at the back view of the spine and measuring the angle formed by the top and bottom vertebrae of the curve. This measurement is called the Cobb angle. 
     Kyphosis is a spinal deformity which can be seen in association with scoliosis. Kyphosis in the thoracic spine means exaggerated kyphotic angle from the spine&#39;s normal kyphotic curve. A spine affected by kyphosis shows evidence of a forward curvature of the vertebrae in the upper back area, which leads to a “humpback” appearance. The Scoliosis Research Society defines kyphosis as a curvature of the spine measuring forty-five degrees or greater on an X-ray. The normal spine has only about twenty to forty-five degrees of curvature in the upper back area. Kyphosis is indicated in lateral X-ray images of the spine by the kyphotic angle, which is the superior angle formed by intersection of two lines drawn on a lateral chest radiogram, tangential to the anterior borders of the second and eleventh intervertebral disc spaces. 
     Using current methods, the Cobb angle is calculated by hand.  FIG. 1  illustrates the hand calculation of the Cobb angle in a coronal view of the spine. Referring to  FIG. 1 , the first step is to find the end-vertebrae which are the vertebrae at the upper and lower limits of the curve which tilt most severely toward the concavity of the curve. Once these vertebrae have been selected, a line is drawn along the upper endplate of the upper body and along the lower endplate of the lower body as shown in  FIG. 1 . The kyphotic angle is also determined by hand. It is defined in the same way as the Cobb angle but is calculated from lateral images. The quality of an image of the spine may be poor, making it difficult if not impossible to locate the limits of the vertebrae, such as when the image is an X-ray image of the spine. 
     SUMMARY OF THE INVENTION 
     According to an exemplary embodiment of the present invention, a method is provided for automatic detection of curvature of a spine and computation of specific angles in images of the spine. The method includes: automatically displaying the curvature of the spine as a line in an image of the spine; and computing at least one of a first angle or a second angle based on the line of the curvature of the spine. 
     According to an exemplary embodiment of the present invention, a system for providing automatic detection of curvature of a spine and computation of specific angles in images of the spine comprises: a memory device for storing a program; a processor in communication with the memory device, the processor operative with the program to: automatically display the curvature of the spine as a line in an image of the spine; and compute at least one of a first angle or a second angle based on the line of the curvature of the spine. 
     According to an exemplary embodiment of the present invention, a method for providing automatic detection of curvature of a spine and computation of one of a Cobb angle or a kyphotic angle in an X-ray image of the spine includes: automatically detecting whether the X-ray image is a lateral view of the spine or a coronal view of the spine; detecting the spine in the X-ray image of the spine; determining the curvature of the spine in the X-ray image of the spine; and when it is determined that the X-ray image is a coronal view of the spine, computing the Cobb angle, and when it is determined that the X-ray image is a lateral view of the spine, computing the kyphotic angle 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will become more apparent to those of ordinary skill in the art when descriptions of exemplary embodiments thereof are read with reference to the accompanying drawings. 
         FIG. 1  illustrates the hand calculation of the Cobb angle in a coronal view of the spine. 
         FIG. 2  is a flowchart showing a method for automatic detection of curvature of a spine and computation of specific angles in images of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 3  is a flowchart showing a method for automatically displaying the curvature of the spine as a line in the image of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 4  shows a display of the curve and the Cobb angle in a coronal view of an X-ray image of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 5A through 5C  illustrate steps to detect the top cut point, according to an exemplary embodiment of the present invention. 
         FIG. 6  illustrates a coronal image and pre-selection of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 7  illustrates improving the contrast of vertebrae of the spine, according to an exemplary embodiment of the present invention. 
         FIGS. 8A through 8E  illustrate the generation of the mask of the spine for coronal images, according to an exemplary embodiment of the present invention. 
         FIGS. 9A through 9C  illustrate the generation of the mask of the spine for lateral images, according to an exemplary embodiment of the present invention. 
         FIGS. 10A through 10C  show displays of the curvature of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 11  illustrates a calculation of the angle of a point in a curve, according to an exemplary embodiment of the present invention. 
         FIGS. 12A and 12B  illustrate curves of the spine and Cobb angle in a coronal image and a lateral image, respectively, according to exemplary embodiments of the present invention. 
         FIG. 13  illustrates edge detection with Gabor filter of two vertebrae of interest in a coronal image, according to an exemplary embodiment of the present invention. 
         FIG. 14  illustrates a computer system for implementing a method for automatic detection of curvature of a spine and computation of specific angles in images of the spine, according to an exemplary embodiment of the present invention. 
         FIG. 15  is a flowchart showing a method for providing automatic detection of curvature of a spine and computation of one of a Cobb angle or a kyphotic angle in an X-ray image of the spine, according to an exemplary embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. 
       FIG. 2  is a flowchart showing a method for automatic detection of curvature of a spine and computation of specific angles in images of the spine, according to an exemplary embodiment of the present invention. Referring to  FIG. 2 , in an optional step  210 , it is automatically determined whether the image is a first view or a second view of the spine. For example, the first view may be a lateral view of the spine, and the second view may be a coronal view of the spine. Examples of images include X-ray images, positron emission tomography (PET) images, computed tomography (CT) images, magnetic resonance imaging (MRI) images, single-photon emission computed tomography (SPECT) images, etc. 
     In step  220 , the curvature of the spine is automatically displayed as a line in an image of the spine.  FIG. 3  is a flowchart showing a method for automatically displaying the curvature of the spine as a line in the image of the spine, according to an exemplary embodiment of the present invention. Referring to  FIG. 3 , in an optional step  310 , the image is pre-processed to improve a contrast of the image. 
     In step  320 , the spine is isolated in the image. When the image is a first view of the spine, isolating the spine in the first view of the spine may comprise determining a top cut point and a bottom cut point, wherein the top cut point corresponds to the beginning of the spine and the bottom cut point corresponds to the bottom of the spine. The step of determining the top cut point may comprise detecting a head in the first view of the image. In an exemplary embodiment of the present invention, detecting the head in the first view of the spine comprises equalizing a histogram, thresholding and segmenting the head. 
       FIGS. 5A through 5C  illustrate a method for detecting the top cut point, according to an exemplary embodiment of the present invention. First, the histogram is equalized. For example, as shown in  FIG. 5A , the histogram may be limited to five grey levels to simplify the image. With a threshold at, for example, half of the new histogram, as shown in  FIG. 5B , the head can be segmented. Referring to  FIG. 5C , the sum of head pixels for each row of this image yields a curve, in which the minimum corresponds to the bottom of the head. Based on the location of the bottom of the head, the beginning of the spine can be detected, which becomes the top cut point. 
     The step of determining the bottom cut point may comprise detecting a pelvis in the first view of the image. The bottom cut point may be determined by the same method used for determining the top cut point. 
       FIG. 6  shows a coronal view of the spine and illustrates pre-selection of the spine, according to an exemplary embodiment of the present invention. Referring to  FIG. 6 , a portion of each side of the original image is removed, and the head and pelvis are removed. For example, if the spine is approximately located in the middle of the image, a quarter of the image on each side of the image may be removed. The head and pelvis may be removed by cutting the original image at the top and bottom cut points, respectively. Pre-selection of the spine results in an image with smaller dimensions than the original image and that shows primarily the spine. In the case of a lateral view of the spine, pre-selection includes removal of the pelvis, but the head may not be removed. 
     In step  330 , the contrast of the image of the spine is improved. In an exemplary embodiment of the present invention, improving the contrast of the image of the spine comprises generating a mask of the spine and improving the contrast of vertebrae of the spine.  FIG. 7  illustrates improving the contrast of vertebrae of the spine, according to an exemplary embodiment of the present invention. Generating a mask of the spine may comprise finding a region of interest and generating a mask to enable removal of artifacts. 
     In an exemplary embodiment of the present invention, finding a region of interest includes dividing the image into a plurality of slices, such that the width of each slice is equal to the width of the image, and the height of each slice is a predetermined number of times smaller than the height of the image. For example, divide the image into slices that have the same width as the image and a height that is 20 times smaller than the image height. For each slice, equalize the histogram to spread the intensity distribution and apply a threshold of a predetermined percentage of a maximum intensity to improve the contrast. For example, for each slice, equalize the histogram to spread the intensity distribution and apply a threshold of seventy-five percent of the maximum intensity to improve the contrast, as shown in  FIG. 7 . Shift each of the slices by one tenth of the height of the slice; and repeating the steps of dividing, equalizing and shifting a predetermined number of times to obtain a set of results. Take the mean of the set of results. 
     When the image is a coronal view of the spine, generating the mask may comprise: generating a binary representation of the image; applying a morphological closing to fill gaps with a small round structuring element in the binary representation of the image; and doing a region labeling and selecting a region of interest in the binary representation of the image. Constraints in the width of the mask may be applied to improve the mask. 
       FIGS. 6A through 6E  illustrate the generation of the mask of the spine for coronal images, according to an exemplary embodiment of the present invention. For example, a mask of the spine can be generated to enable removal of substantially all artifacts from the ribs. The following steps may be used for coronal images of the spine. First, find the region of interest: Divide the image into slices, for example, having the same width as the image and a height that is twenty times smaller than the image height. For each slice, equalize the histogram to spread the intensity distribution and apply a threshold of, for example, seventy-five percent of the maximum intensity to improve the contrast. Second, generate a mask: Generate a binary of the image, for example, each non-black pixel becomes true, false elsewhere, as shown in  FIG. 8A . Apply a morphological closing (dilation+erosion) to fill the gaps with a small round structuring element, as shown in  FIG. 8B . 
               D   ⁡     (     A   ,   B     )       =       A   ⊕   B     =       ⋃     β   ∈   B       ⁢     (     A   +   β     )                       E   ⁡     (     A   ,   B     )       =       A   ⊖     (     -   B     )       =       ⋂     β   ∈   B       ⁢     (     A   -   β     )               
Do a region labeling and select the region of interest as shown in  FIG. 8C . Dilate as shown in  FIG. 8D . Apply constraints in the width of the mask to improve the mask as shown in  FIG. 8E . Fill the black parts (width equal to 0). Remove the holes (width smaller than the average width). Remove the bumps (width larger than the average width).
 
       FIGS. 9A through 9C  illustrate the generation of the mask of the spine for lateral images, according to an exemplary embodiment of the present invention. When the image is a lateral view of the spine, generating the mask may comprise: generating a binary representation of the image; doing a region labeling and selecting a region of interest in the binary representation of the image; separately extracting a right boundary and a left boundary of the region as vectors; selecting the smoothest curve; and drawing a mask following the curve and adding a predetermined value representing a width of the spine. 
     Referring to  FIGS. 9A through 9C , for lateral images: Generate a binary representation of the image. Do region labeling, and select the region of interest as shown in  FIG. 9A . Extract the right and the left boundaries of the region separately as vectors. Select the smoothest curve and use it to represent the back, as shown in  FIG. 9B . Draw a mask following the curve and adding the width of the spine, which may be arbitrarily defined, as shown in  FIG. 9C . 
     Having isolated the spine, it is easier to improve the contrast of the vertebrae. Extract a section on the bottom of the image. For each section, apply histogram equalization and stretching. For example, if an image has a range of grey level between g 1  and g 2 , it is possible to increase the contrast by using a larger range of values: from 0 to 255. Letting x=original grey-level scale of the image, and y=resulted grey-level scale, the transformation can be expressed as follows: y=255×(x−g i )/(g 2 −g i ) . 
     In each section, extract the resulting middle line and shift the band of one line above. This technique works on the histogram locally in the image and may result in improved contrast. 
     In step  340 , boundaries of the spine are detected from the improved image. In an exemplary embodiment of the present invention, detecting boundaries of the spine from the improved image comprises: applying a threshold to the contrast-enhanced image; doing a region labeling of the binary image and selecting the spine; along each row of the spine region, storing the minimum and maximum x-coordinates in two right and left vectors; and smoothing the two vectors with the minimum and maximum x-coordinates using a smoothing window of a predetermined height. For example, the predetermined height may be a tenth of the image height. In step  350 , a middle line is displayed in the spine that represents the curvature of the spine in the image. 
       FIGS. 10A through 10C  show displays of the curvature of the spine, according to an exemplary embodiment of the present invention. Detect the boundaries of the spine from the improved image by generating a binary of the image followed by a region labeling and extraction of the region of interest, as shown in  FIG. 10A . Then, extract the right and the left boundaries separately as vectors (vecRight and vecLeft) as shown in  FIG. 10B . Smooth these two vectors with a large width (a tenth of the height of the image). The smooth array R of a vector A can be expressed as follows. 
     
       
         
           
             
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     where N=number of elements in A, W=width of the smooth operation. 
     Extract the mean line of these two vectors. For each row i of the image: vecCurve(i)=[vecRight(i)+vecLeft(i)]/2. By converting this resulting vector (vecCurve) into a line in the image, a representation of the curve of the spine is obtained as shown in  FIG. 10C .  FIGS. 12A and 12B  illustrate curves of the spine and Cobb angle in a coronal image and a lateral image, respectively, according to exemplary embodiments of the present invention. 
     Referring to  FIG. 2 , in step  230 , the first angle or the second angle is computed based on the line of the curvature of the spine. For example, the first angle may be the Cobb angle that is computed for coronal images, and the second angle may be the kyphotic angle that is computed for lateral images. 
       FIG. 4  shows a display of the curve and the Cobb angle in a coronal view of an X-ray image of the spine, according to an exemplary embodiment of the present invention. The Cobb angle may be computed as the sum of the two largest angles between the curve of the spine and horizontal line as shown in  FIG. 4 . 
     In an exemplary embodiment of the present invention, the first angle is the Cobb angle, and computing the Cobb angle based on the curvature of the spine comprises: determining the global curve of the spine; finding the maximum positive and negative angles from the curve by calculating an angle at each point of the curve; and computing the Cobb angle by summing the absolute value of the most negative and positive angles of the curve. Calculating the angle at each point of the curve may comprise: computing the angle using a tangent of the curve at the specified point and a horizontal line; locally applying a Gabor filtering along the direction set by the computed angle; estimating an orientation from endplates of the vertebrae based on the Gabor filtering; and setting the orientation as the Cobb angle.  FIG. 13  illustrates edge detection with Gabor filter of two vertebrae of interest in a coronal image, according to an exemplary embodiment of the present invention. 
     In an exemplary embodiment of the present invention, the second angle is the kyphotic angle, and computing the kyphotic angle based on the curvature of the spine comprises: determining a global curve of the spine; finding the maximum positive and negative angles from the curve by calculating an angle at each point of the curve; and computing the kyphotic angle by summing the absolute value of the most negative and positive angles of the curve. Calculating an angle at each point of the curve may comprise: computing the angle using a tangent of the curve at the specified point and a horizontal line; locally applying a Gabor filtering along the direction set by the computed angle; estimating an orientation from endplates of the vertebrae based on the Gabor filtering; and setting the orientation as the kyphotic angle. 
     To calculate the first or second angle (e.g., Cobb or kyphotic), two angles are needed: the positive and the negative angles which tilt the most.  FIG. 9  illustrates a calculation of the angle of a point in a curve, according to an exemplary embodiment of the present invention. For each point i of the curve, calculate the angle (α) between the tangent and the horizontal line. Then, separate the positive angles (α p ) from the negative ones (α N ). Determine the two maximum values and the sum of these values gives the specific angle: Specific angle=max (α p )+|max (α N )| 
       FIG. 14  illustrates a computer system for implementing a method for automatic detection of curvature of a spine and computation of specific angles in images of the spine, according to an exemplary embodiment of the present invention. Examples of images include X-ray images, positron emission tomography (PET) images, computed tomography (CT) images, magnetic resonance imaging (MRI) images, single-photon emission computed tomography (SPECT) images, etc. 
     Referring to  FIG. 14 , a computer system  101  for implementing a method of automatic detection of curvature of a spine and computation of specific angles in images of the spine can comprise, inter alia, a central processing unit (CPU)  109 , a memory  103  and an input/output (I/O) interface  104 . The computer system  101  is generally coupled through the I/O interface  104  to a display  105  and various input devices  106  such as a mouse and keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus. The memory  103  can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof. The present invention can be implemented as a routine  107  that is stored in memory  103  and executed by the CPU  109  to process the signal from the signal source  108 . As such, the computer system  101  is a general purpose computer system that becomes a specific purpose computer system when executing the routine  107  of the present invention. 
     The computer platform  101  also includes an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device. 
     In an exemplary embodiment of the present invention, a system for providing automatic detection of curvature of a spine and computation of specific angles in images of the spine comprises a memory device ( 103 ) for storing a program, and a processor ( 109 ) in communication with the memory device ( 103 ). The processor ( 109 ) is operative with the program to automatically display the curvature of the spine as a line in an image of the spine, and compute at least one of a first angle or a second angle based on the line of the curvature of the spine. The processor ( 109 ) may be further operative with the program code to automatically detect whether the image is a lateral view of the spine or a coronal image of the spine. 
     The processor ( 109 ) may be operative with the program code to: isolate the spine in the image; improve the contrast of the image of the spine; detect boundaries of the spine from the improved image; and display a middle line in the spine that represents the curvature of the spine in the image. 
     The processor ( 109 ) may be operative with the program code to: divide the image into a plurality of slices or sections, wherein a width of each slice or section is equal to a width of the image, and wherein a height of each slice or section is a predetermined number of times smaller than a height of the image; for each slice or section, equalize a histogram to spread the intensity distribution and apply a threshold of a predetermined percentage of a maximum intensity to raise a contrast; shift each of the slices by one tenth of the height of the slice; and repeat the steps of dividing, equalizing and shifting a predetermined number of times to obtain a set of results; and calculate a mean of the set of results. 
     The processor ( 109 ) may be operative with the program code to: generate a binary representation of the image; do a region labeling and select a region of interest in the binary representation of the image; separately extract a right boundary and a left boundary of the region as vectors; select the smoothest curve; and draw a mask following the curve and add a predetermined value representing a width of the spine. 
     It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures may be implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the present invention is programmed. Given the teachings of the present invention provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the present invention. 
       FIG. 15  is a flowchart showing a method for providing automatic detection of curvature of a spine and computation of one of a Cobb angle or a kyphotic angle in an X-ray image of the spine, according to an exemplary embodiment of the present invention. 
     Referring to  FIG. 15 , in step  1510 , automatically detecting whether the X-ray image is a lateral view of the spine or a coronal view of the spine. In step  1520 , the spine is detected in the X-ray image of the spine. In step  1530 , the curvature of the spine is determined in the X-ray image of the spine. In step  1540 , when it is determined that the X-ray image is a coronal view of the spine, the Cobb angle is computed, and when it is determined that the X-ray image is a lateral view of the spine, the kyphotic angle is computed. 
     Although exemplary embodiments of the present invention have been described in detail with reference to the accompanying drawings for the purpose of illustration, it is to be understood that the inventive processes and systems are not to be construed as limited thereby. It will be readily apparent to those of reasonable skill in the art that various modifications to the foregoing exemplary embodiments may be made without departing from the scope of the invention as defined by the appended claims, with equivalents of the claims to be included therein.