Patent Application: US-201314376643-A

Abstract:
methods and arrangements for detecting osteoarthritis relate to image processing for enhancing , visualizing and quantifying the fibrillation structure of cartilage using endoscopes . a structure enhancement method comprises obtaining input data , conversion to intensity data , preprocess filtering , intensity fluctuation filtering and contrast enhancement . the degeneration is quantified by a degeneration index algorithm , applied to the structure enhanced image . results are then compiled in an output frame presentation .

Description:
the main steps of the tissue structure enhancement method according to the invention can be summarized as : obtaining input data , conversion to intensity data , preprocess filtering , intensity fluctuation filtering , contrast enhancement and output frame presentation ( fig1 ). these different steps are described below , together with a description of the di calculation . circuitry and processing within an endoscopic video camera are well suited to perform algorithm calculations and graphically present the result as an enhancement to the live arthroscopic image . for purposes of displaying the best endoscopic image on the surgical monitor , the raw red , green and blue ( rgb ) signals collected by the camera and endoscope are , in endoscopic cameras used today , modified by both linear and non - linear transformations . for example , edge enhancement , color correction and gamma correction . such transformations may affect the quality of algorithm calculations . on the other hand , automatic exposure , white balance , and defective pixel correction are camera processes applied to the rgb signals that improve the repeatability and quality of the calculations . given these constraints , fig2 shows where in the video processing path the rgb signals are taken for input to the algorithm formulas shown below . the calculations are performed in a field programmable gate array ( fpga ) for each pixel in every video frame . there are indications that some tissues undergo spectral changes during degeneration , for instance cartilage during oa progression , but in the most specific solution , the enhancement algorithm uses only intensity data . the rgb data from the input frame is therefore reduced to a single intensity frame , preferably by calculating the mean value of the red , green and blue channel values of the input frame . an alternative solution is to select one of the three channels . this selection influences the tissue level at which the structure is enhanced . the structure enhancement algorithm is based on local fluctuations in intensity , caused by the light interacting with the fibrillated tissue , leading to tissue structure dependent fluctuations in the back - scattered light . partly to bring out the faint details in these fluctuations , covered by noise , and partly to adjust to the desired level of fibrillation to enhance , a preprocessing filter is applied . this is typically an averaging or gaussian low - pass filter . filter size and other characteristics are chosen depending on image resolution , tissue type and what level of the fibrillations to enhance . a typical choice for arthroscopic 960 × 540 pixel video / image assessment of degenerated cartilage is a 10 × 10 averaging filter . the central part of the structure enhancement algorithm is the application of a local intensity fluctuation enhancement operator . this is typically performed by using a standard image filtering approach with a specific x × y pixel kernel . the kernel can be applied to the preprocessed image pixel by pixel or in a stepwise manner , for instance to reduce computing demands . the calculation can be done in separable horizontal and vertical steps . the kernel calculation is based on deriving a single measure related to intensity variation , for instance variance ( equation 1 ), standard deviation , entropy ( equation 2 ) or some other statistical measure of variation . here i se is the kernel output value describing structure enhanced values , n the number of kernel values , p i the kernel pixel values and μ the kernel pixel average value . kernel size depends on the same geometrical and tissue dependent factors as in the preprocessing step , but a typical example for arthroscopic 960 × 540 pixel video / image assessment of degenerated cartilage is to use a 5 × 5 variance or standard deviation based kernel . the output image from this processing step will be referred to as the structure enhanced image . if visualizing the structure enhanced image , a contrast enhancement may be appropriate . this could include mapping the result onto the dynamic range [ 0 255 ] according to equation 3 . i ce = 255 ( i se − t 1 )( t 2 − t 1 ) ( 3 ) here i ce is the contrast enhanced image and the t values are contrast level thresholds . the output image from this processing step will be referred to as the contrast enhanced image . in fig3 examples of contrast enhanced images are shown . the images are from an ex vivo study on knee condyles , removed from patients ( n = 11 ) undergoing total knee replacement because of oa . left image shows a normal cartilage surface after application of the structure enhancement algorithm . right image shows corresponding image from an oa cartilage region . the local or global pixel values in the structure enhanced image , before or after contrast enhancement , can be reduced to a single di value based on variance analysis . more advanced approaches include pattern recognition or fourier domain analysis to quantify pixel fibrillation . the di makes it possible to quantitatively compare different degeneration stages of tissue . in fig4 results from a clinical study on routine knee arthroscopy patients ( n = 33 ) are shown . here , di values were calculated using standard deviation of structure enhanced images , derived from 33 sites of normal cartilage and from 58 degenerated cartilage sites . the figure shows mean ± standard deviation . the difference was statistically significant ( p & lt ; 0 . 05 ). generating an output frame based on the structure or contrast enhanced output images can be made in many different ways . one example is using a picture in picture approach , where the processed image is presented together with the input frame ; another is showing the processed result as an overlay to the input frame . in the latter example the output image values are applied to selected regions of the input image . regions can for instance be those that are not too bright because of over exposure , too dark because of insufficient illumination , or where the derived output image values give rise to a local di value that is higher than a specific threshold . worth noting is that the overlay may consist of the enhanced output image values themselves or be presented in a simplified fashion using a specific colour or a colour according to a look - up - table . image examples are shown in fig5 - 6 . fig5 shows examples of applying structure and contrast enhancement , followed by di calculation , to images of sandpaper surfaces of varying degrees of surface roughness . higher di values are seen for higher degrees of roughness , corresponding to higher degrees of tissue degeneration . in left part of fig6 , a normal arthroscopical view of cartilage is presented . in the right part of the figure , the same view is seen after the structure and contrast enhancement algorithms have been applied . the oa cartilage fibrillation structure is enhanced and visualized . in this example the enhancement algorithm has only been applied to bright pixels , leaving darker pixels untreated . the method and arrangement according to the present invention has been described as performed within a camera . this is a convenient solution . however , as understood by the skilled person , the necessary calculations can be performed in any suitable equipment , such as external computers or dedicated external devices . such solutions can for instance be attractive if to use older types of cameras , or for off - 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