Patent Application: US-92115306-A

Abstract:
the invention is a method for estimating a skeletal maturity value of a human from a radiograph of one or more bones in the hand . the borders of the bones are represented by shape points , which are subjected to principal component analysis . image intensities are sampled at points located relative to the shape point , and also compressed with pca . from the features a skeletal maturity value is determined .

Description:
in the following are given some details to describe exactly how to implement the steps mentioned so far . the input to the method ( fig5 , s 1 ) is a digital image , for instance in the form of a tiff file with a number indicating the intensity at every pixel . the image can be obtained from a radiographic film , which has been digitised using an optical scanner , or it can be obtained from a cr image recording it on a phosphor plate which is read off and digitised by the cr equipment , or by using a direct digital radiography detector outputting the intensities directly from the electronics . the image can also be from dexa scanner of good spatial resolution and can then either be a mono - energetic image or a subtracted image . associated with the image is also the spatial resolution in mm per pixels . also the sex of the child has to be known to derive the bone age . the identification of the bone borders ( fig5 , s 2 ) can be performed with active appearance models ( aam ). in the first step the process searches simultaneously for 3 or 4 of the metacarpals because they form a pattern , which is simple to search for exhaustively at all locations and in all orientations , see [ 12 ] for details . the process searches both for left and right hands and the best search - result determines the interpretation . subsequently the process locates the remaining bones by predicting a start search location based on the bones found so far . such a complete framework for aam reconstruction of bones in the adult hand was reported in reference [ 5 ]. in the present method the age is unknown and every time a bone is searched the process can apply several models corresponding to different maturity stages . for instance the process can use three models covering the age ranges 2 - 6 years , 6 - 13 years and 13 - 18 years , and the model that fits best is the selected reconstruction . in some of the younger models , the epiphysis is separated form the metaphysis . once the process has reconstructed the border , it extracts the shape points to represent the bone border ( fig5 , s 3 ). if the reconstruction model is aam or asm , this already provides shape points from the underlying pca shape model . otherwise the process can fit the border to a pca - based shape model , i . e . parameterise the border as the mean shape transformed to a given pose ( using a translation vector , a rotation angle and scaling factor , see [ 13 ]), and determine a deformation in terms of the shape parameters . asm can be used to implement this as demonstrated in [ 10 ]. the extraction of a vector of sampled image intensities ( fig5 , s 4 ) is illustrated in fig3 and 4 , which show the mean shape points in large squares ( 1 ), defined at the border of the bone . auxiliary points can be defined away from the bone border , shown with smaller squares in fig3 and 4 . in the interior ( 2 ), these points can be defined as interpolations between border points . at the exterior ( 3 ), each point can be defined relative to one of the border points at a certain angle and distance from this . the angle is defined relative to the axis of the bone and the distance is computed relative to the size of the bone . the border and auxiliary points define the corners of the triangles ; the auxiliary points ensure that the triangles span also a margin of the bone and avoids occurrence of triangles with very small angles . inside the triangulated area of the mean shape the process places sampling points for instance in a regular grid , as shown with the smallest points ( 4 ) in fig3 and 4 . as exemplified in the embodiment in fig4 , which is a detailed view of part of fig3 , only part of the triangulated area can be used for sampling since the most important changes of the density are known to be in this part of the bone where growth occurs . the process can in one embodiment also form texture features by defining a texture vector in each location of the image ( fig5 , s 5 ). each element of the texture vector can reflect a certain wavelength and direction . the standard choice is the gabor filters [ 9 ], but most other texture measures would give the same effect . a wavelength of 1 or 2 mm is appropriate to catch the signal of the border between the epiphysis and the metaphysis and its fusion . four to six directions are appropriate . in the simplest scheme , four directions and a single wavelength is used . this means that the image is filtered with four gabor filters , each comprising a real and an imaginary part . the real and imaginary outputs are squared and added to form four energies per location . the square root can be taken to compress the dynamic range . the textures can be normalised with some number indicative of the image contrast derived from the same image , e . g . the standard deviation of the intensities in the bone region , leading to the final four bands of the texture image . in an embodiment , the process samples four texture band images at the sampling points e . g . as those in fig3 or 4 , and the vector of samplings is subjected to a pca defined previously on a training set . j . m . tanner , r . h . whitehouse , n . cameron , w . a . marshall , m . j . r . healy , and h . goldstein : assessment of skeletal maturity and prediction of adult height ( tw2 method ). academic press , london , 2nd edition , 1975 . m . niemeijer : automating skeletal age assessment , master &# 39 ; 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