This application claims the benefit of provisional application No. 60/258,182, filed Dec. 22, 2000. 
    
    
     FIELD OF THE INVENTION 
     This invention relates to methods for analyzing data obtained by irradiating biological tissues or organs. 
     BACKGROUND OF THE INVENTION 
     Imaging methods such as ultrasound (US), magnetic resonance imaging (MRI), and computer tomography (CT), are widely used because of their ability to non-invasively image body organs and tissues with minor deleterious effects. In these techniques, an organ or tissue is irradiated with sonic or electromagnetic waves. The waves reflected or scattered by the organ or tissue are recorded and processed into a digital image. 
     SUMMARY OF THE INVENTION 
     The present invention is based upon the finding that healthy tissue may be distinguished from its malignant counterpart by the way the tissue reflects radiation energy. The organization of reflecting members in a healthy tissue is more spatially regular than in malignant tissue. The invention may thus be used in the diagnosis of cancer or other disorders involving alterations in the organization or texture of a tissue, such as the presence of a liquid filled cyst. 
     In accordance with the invention, a tissue is irradiated and the reflected waves are detected. An analysis is performed on the reflected waves in order to generate one or more parameters indicative of a degree of spatial disorder of the reflecting members in the tissue. In one embodiment of the invention, a calculated parameter value is compared to a predetermined threshold. If the calculated parameter value exceeds the threshold, the tissue is determined to be malignant. A tissue having a calculated parameter value less than the threshold is a healthy tissue. In another embodiment, one or more calculated parameters are input to an expert system such as a neural network. The neural system makes an assessment as to whether the tissue is healthy or malignant based upon the input parameter values. Expert systems are known, for example as disclosed in Kadah et al., IEEE Transactions, vol. 15, No. 4, pages 472-473, 476-477, August 1996. 
     The invention may be carried out using any form of irradiation such as electromagnetic radiation or sonic radiation. In particular, the invention may be applied to waves reflected in an ultrasound, CT, or MRI procedure. 
     The analysis of the detected reflected waves may be performed using any mathematical method for evaluating a degree of periodicity. The analysis may thus involve, for example, a Fourier analysis, a wavelet analysis, or an entropy analysis. The analysis may be performed on complex raw data obtained from the reflected waves. Alternatively, an image may be generated from the complex raw data, and the analysis performed on the image. 
     In another of its aspects, the invention provides a method for generating an image of the tissue based upon the reflected or scattered waves using non-Fourier analysis. This produces an image of better resolution and contrast than is obtainable by a Fourier analysis of the reflected or scattered waves, which is the present standard of existing signal processing algorithms. Methods for non-Fourier analysis of scattered or reflected waves are known in the art, for example, as disclosed in Degraaf, S., IEEE Transactions on Image Processing, Vol. 7, No. 5, May 1998. As shown in this reference, the non-Fourier analysis may utilize for example, Capon&#39;s minimum variance method. 
     Thus in its first aspect, the invention provides a method for assessing a spatial regularity of reflecting members in a tissue, comprising steps of: 
     (a) irradiating the tissue; 
     (b) detecting waves reflected by the tissue; and 
     (c) calculating one or more parameters indicative of a degree of spatial disorder of reflecting members in the tissue based upon the reflected waves. 
     In its second aspect, the invention provides, a method for determining whether a tissue is malignant comprising steps of; 
     (a) irradiating the tissue; 
     (b) detecting waves reflected by the tissue; 
     (c) calculating a parameter indicative of a degree of spatial disorder of reflecting members in the tissue based upon the reflected waves; and 
     (d) comparing the parameter to a predetermined threshold; the tissue being malignant if the parameter exceeds the predetermined threshold. 
     In its third aspect, the invention provide a method for determining whether a tissue is malignant comprising steps of; 
     (a) irradiating the tissue; 
     (b) detecting waves reflected by the tissue; 
     (c) calculating one or more parameters indicative of a degree of spatial disorder of reflecting members in the tissue based upon the reflected waves; and 
     inputting the one or more parameters into an expert system so as to generate an assessment as to whether the tissue is malignant. 
     In its fourth aspect, the invention provides a system for assessing a spatial regularity of reflecting members in a tissue, comprising: 
     (a) a wave source configured to irradiate the tissue; 
     (b) a wave detector configured to detect waves reflected by the tissue; and; 
     (c) a processor configured to calculate a parameter indicative of a degree of spatial disorder of reflecting members in the tissue based upon the reflected waves. 
     In its fifth aspect, the invention provides, a system for determining whether a tissue is malignant comprising: 
     (a) a wave source configured to irradiate the tissue; 
     (b) a wave detector configured to detect waves reflected by the tissue; 
     (c) a processor configured to calculate a parameter indicative of a degree of spatial disorder of reflecting members in the tissue based upon the reflected waves. 
     In its sixth aspect, the invention provides a method for determining whether a tissue is malignant comprising steps of; 
     (a) irradiating the tissue; 
     (b) detecting waves reflected or scattered by the tissue; 
     (c) performing an analysis of the reflected or scattered waves; 
     (d) inputting the results of the analysis into an expert system. 
     In yet another aspect, the invention provides a method for generating an image of the tissue, comprising steps of: 
     (a) irradiating the tissue; 
     (b) detecting waves reflected by the tissue; and 
     performing a non-Fourier analysis of the detected waves so as to produce an image of he tissue. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     In order to understand the invention and to see how it may be carried out in practice, a preferred embodiment will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which: 
     FIG. 1 shows a system for analyzing reflected waves in accordance with one embodiment of the invention; 
     FIG. 2 shows a Fourier analysis of healthy (a,d), malignant (b,e) and benign (c,f) ovarian tissue in accordance with one embodiment of the invention; 
     FIG. 3 shows a wavelet analysis of healthy (a); malignant (b); benign (c) ovarian tissue, in accordance with another embodiment of the invention; and 
     FIG. 4 shows an entropy analysis of healthy malignant and benign ovarian tissue. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1 shows a system for analyzing biological tissues or organs in accordance with one embodiment of the invention. A transducer  100  contains a wave generator  105  for generating waves. The generated waves may be sonic waves or electromagnetic waves. The transducer also comprises an array of detectors  110  that detect reflected waves. A processor  115  is used to select the properties of the generated waves (e.g. amplitude and wavelength) via a signal  118  input to the wave generator  105 . The wave generator  105  is used to produce generated waves  120  that irradiate a tissue or organ  125 . Waves  130  reflected by the organ or tissue  125  are detected by the detectors  110  in the transducer  105 . The wave detected by each detector is converted by the detector into an analog voltage dependent signal that is sampled by an analog to digital converter  140 . The digital samples  142  are then input to the processor  115 . The processor  115  calculates a phase for each sample based upon the signal  118  and stores the digital samples in a memory  135  in the form of complex raw data R(x,y). 
     A second processor  145  is configured to receive the complex raw data R(x,y) from the memory  135  and process the complex raw data into an image I(x,y) as is known in the art. The image may be displayed on a display such as a CRT  150 . 
     In accordance with the invention, a third processor  155  is configured to analyze the tissue by processing either the raw data R(x,y) or the processed image data I(x,y). The results of the analysis may be displayed on a display such as the CRT  160 . 
     EXAMPLES 
     Example 1 
     Fourier Analysis of Ultrasound Images 
     FIG. 2 shows an ultrasound image I(x,y) of human ovary tissue from a healthy ovary (a) from a malignant ovarian tumor (b), and a benign ovarian tumor (c), as determined by histological examination of the tissues. (e) (f) and (g) show the Fourier transform F(y,ω)=∫I(x,y)e iwx dx of a 30×30 pixel square from the image shown in (a) (b) and (c), respectively. The energy of each Fourier transform was measured by evaluating the sum Σ|∂FI∂y| over the range of 1≦y≦28 and 34≦ω≦64. The energy calculated for the normal tissue (a,d) was 3, for the malignant tissue (b,e) 8, and for the benign tissue, 3. An analysis of 30 ovarian tissue showed that by this method of calculating energy, healthy ovarian tissues have an energy in the range of about 2 to 4, while malignant ovarian tissues have an energy in the range of about 7-9. Ovarian tissues having a benign growth were indistinguishable from healthy ovarian tissues. Other methods may be used for measuring energy may also be used in accordance with the invention such as calculating a volume under the Fourier transform. 
     Example 2 
     Wavelet Analysis of Ultrasound Images 
     FIG. 3 shows a wavelet analysis of the three images I(x,y) shown in FIG.  2 . The 30×30 pixel square from each image was input to the wavelet analysis software of the Matlab™ wavelet toolbox. The B-orthogonal filter was used with a decomposition level equal to 1. The output of this software is four matrices known as the principle image coefficients (A), horizontal coefficients (H), vertical image coefficients (V) and the diagonal coefficients (D). FIG. 3 shows the contour graph of the coefficients of the A matrix obtained for each image. The maximum of each contour graph was used as an index. The index of the malignant tissue is  204 , of the benign tissue  162  and the healthy tissue  90 . An analysis of 30 ovarian tissues showed that malignant tissues have indices 2-2.5 times those of healthy tissues. Other indices maybe also used in accordance with the invention when using wavelet analysis such as the maximum coefficient in sum of the H, V, and D coefficient matrices. Other filters may be used in accordance with the invention such as a Mexican hat filter, as are known in the art. 
     Example 3 
     Entropy Analysis of Ultrasound Images 
     FIG. 4 shows the results of an analysis of entropy in 60 images of ovaries. The state (healthy, benign or malignant) was determined for each ovary by histological methods. For each image, a 30×30 pixel square was selected and an entropy E was calculated for each square as follows. For each pixel I(x,y), a parameter A(x,y) was calculated by          A        (     x   ,   y     )       =         1   n     ∑     |       I        (     x   ,   y     )       -     I        (       x   ′     ,     y   ′       )              |   2                              
     was calculated, where the sum extends over all pixels (x′,y′) in the square neighboring the pixel (x,y), and n is the number of pixels neighboring the pixel (x,y). The entropy was then calculated as the average of the A(x,y) over the entire square. As shown in FIG. 4, images of healthy ovaries were found to have the lowest entropy (in the range of 2 to 4.3). Images from malignant ovaries have high entropies (6.9-8.3). Images from benign tissues have intermediate to high values of entropy (4.9-8.3).
A method and system for assessing a spatial regularity of reflecting members in a tissue. The method comprises irradiating the tissue and detecting waves reflected by the tissue. One or more parameters are then calculated, based upon the reflected waves, indicative of a degree of spatial disorder of reflecting members in the tissue.