Image analysis – Applications – Biomedical applications
Reexamination Certificate
1999-10-07
2002-07-02
Johns, Andrew W. (Department: 2621)
Image analysis
Applications
Biomedical applications
C382S130000
Reexamination Certificate
active
06415046
ABSTRACT:
TECHNICAL FIELD
The present invention relates to methodologies for detecting tissue pathology, more particularly, to a method and apparatus for the early detection of tissue pathology using a wavelet decomposition method on tissue data obtained by using a multi-dimensional non-invasive imaging technique. Such method and apparatus provides a tool for physicians and researchers to diagnose and thus treat early stages of diseases or other disorders affecting suspect tissue to minimize irreversible tissue or other pathological damage caused by such disease.
(By “pathology” I mean any abnormality or disorder of a tissue, muscle, organ, etc. In using the term “tissue” in the above paragraph and throughout this disclosure, “tissue” means tissue, muscle, organ, etc.)
BACKGROUND OF THE INVENTION
Detection of diseased tissue by other than histological or biochemical means is a challenge for non-invasive imaging techniques. Pathologies of organ, muscle and tissue, such as various forms of cardiomyopathy, represent a group of diseases in which a non-invasive imaging technique to distinguish normal from abnormal tissue would be of particular importance. Texture analysis of organs, muscles or tissue, such as myocardium, is an approach to tissue characterization based on the spatial distribution of amplitude signals within a region-of-interest (ROI). While I use ultrasound and myocardium as the tissue of interest to describe how to make and use my invention, the invention can be used for images obtained by other multi-dimensional non-invasive imaging techniques, and to study other tissues, such as skeletal muscle, liver, pancreas, kidneys, and arterial wall linings. My invention is especially suitable when only a small ROI is available for analysis, such as a 16×16 ROI, as it has been stated by others that statistical methods are less reliable for small ROI's as noise within the signal has a significant effect. However, my invention is also suitable for large ROI'S and, I speculate, is a better detector than statistical methods.
The characterization of myocardial tissue itself by ultrasound was attempted in 1957 where excised human hearts were used to distinguish infarcted from normal myocardium.
Other multi-dimensional non-invasive imaging techniques include magnetic resonance imaging (MRI), radionuclide imaging, and computer axial tomography (CAT Scan). Moreover, three-dimensional or higher order imaging may also be employed. However, in lieu of a two-dimensional wavelet transform, a three-dimensional wavelet should be substituted when a three-dimensional image is analyzed.
Methodologies to: characterize myocardium by ultrasound include quantitative estimates of frequency-dependent myocardial attenuation and backscatter (as referred to in Miller J G, Perez J E, Sobel B E, “Ultrasonic characterization of myocardium,”
Progress in Cardiovascular Diseases
, copyright September/October 1985; XXVIII(2):85-110, all of wich is incorporated herein by reference) and have been subsequently used to distinguishnormal from abnormal myocardium (as referred to in Masuyama T, Valantine H R, Gibbons R, Schnittger I, Popp R L, “Serial measurements of integrated ultrasonic backscatter in human cardiac allografts for the recognition of acute rejection,”
Circulation
, copyright March 1990;81(3):829-839; Wickline S A, Thomas J L III, Miller J G, Sobel B E, Perez J E, “Sensitive detection of the effects of reperfusion on myocardium by ultrasonic tissue characterization with integrated backscatter,”
Circulation
, copyright 1986;74:389-400; Sagar, K B, Rhyne T L, Pelc L R, Warltier D C, Wann L S, “Intramyocardial variability in integrated backscatter: effects of coronary occlusion and reperfusion,”
Circulation
, copyright 1987;75:436-442; and, Sagar K B, Pelc L R, Rhyne T L, Komorowski R A, Wann L S, Warltier D C, “Role of ultrasonic tissue characterization to distinguish reversible from irreversible myocardial injury,”
JASE
, copyright November/December 1990;3(6):471-477, all of which is incorporated herein by reference).
For the purposes of the present invention, the definition of image texture is “an attribute representing the spatial arrangement of the gray levels of the pixels in a region” (as referred to in “IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology,”
IEEE Press
, copyright Mar. 26, 1990; 7.14, all of which is incorporated herein by reference). Tissue pathology which changes microscopic anatomical structure changes myocardial ultrasound texture (speckle), and echocardiographic texture does contain tissue structure related information (as referred to in Smith S W, Wagner R F, “Ultrasound speckle size and lesion signal to noise ratio: verification of theory,”
Ultras Imag
, copyright 1984; 6:174; and, Wagner R F, Smith S F, Sandrick J M, Lopez H, “Statistics of speckle in ultrasound B-scans,”
IEEE Trans Sonics Ultras
, copyright May 1983; 30(3):186-163, all of which is incorporated herein by reference).
Attempting to numerically quantitate texture has been generally problematic with respect to the myocardium and, I speculate with respect to other organs, muscles and tissue. Quantization of texture using statistical techniques has been performed to identify various cardiomyopathic abnormalities, including myocardial contusion (as referred to in Skorton D J, Collins S M, Nichols J, Pandian N G, Bean J A, Kerber R E, “Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of experimental myocardial contusion,”
Circulation
, copyright July 1983;68(1):217-223, all of which is incorporated herein by reference), amyloid infiltration (as referred to in Pinamonti B. Picano E, Ferdeghini E M, Lattanzi F, Slavich G, Landini L, Camerini F, Benassi A, Distante A, L'Abbate A, “Quantitative texture analysis in two-dimensional echocardiography: application to the, diagnosis of myocardial amyloidosis,”
JACC
, copyright September 1989; 14(3):666-671, all of which is incorporated herein by reference) hypertrophic cardiomyopathy (as referred to in Chandrasekaran K, Aylward P E, Fleagle S R, Burns T L, Seward J B, Tajik A J, Collins S M, Skorton D J, “Feasibility of identifying amyloid and hypertrophic cardiomyopathy with the use of computerized quantitative texture analysis of clinical echocardiographic data,”
JACC
, copyright Mar. 15, 1989;13(4):832-840, all of which is incorporated herein by reference), coronary ischemia (as referred to in Picano E, Faletra F, Marini C, Paterni M, Danzi G B, Lombardi M, Campolo L, Gigli G, Landini L, Pezzano A, Distante A, “Increased echodensity of transiently asynergic myocardium in humans: a novel echocardiographic sign of myocardial ischemia,”
JACC
, copyrights January 1993;21(1):199-207, all of which is incorporated herein by reference), myocardial non-viability (as referred to in Marini C, Picano E, Varga A, Marzullo P, Pingitore A, Paterni M, “Cyclic variation in myocardial gray level as a marker of viability in man: a videodensitometric study,”
Eur Heart J
, copyright March 1996;17:472-479, all of which is incorporated herein by reference), transplant rejection (as referred to in Stempfle H, Angermann C E, Kraml P, Schutz A, Kemkes B M, Theisen K, “Serial changes during acute cardiac allograft rejection: quantitative ultrasound tissue analysis versus myocardial histologic findings,”
JACC
, copyright July 1993;22(1)310-317, all of which is incorporated herein by reference) and myocarditis (as referred to in Ferdeghini E M, Pinamonti B, Picano E, Lattanzi F, Bussani R, Slavich G, Benassi A, Camerini F, Landini L, L'Abbate A, “Quantitative texture analysis in echocardiography: application to the diagnosis of myocarditis,”
J Clin Ultrasound
, copyright June 1991;19:263-270, all of which is incorporated herein by reference). These published reports used first- or second-order gray level histogram statistics for evaluation (usually 8-bit information), including mean gray level, standard deviation of the mean, skewness (deviation of the pixel distribution from a symmetrical shap
Areanx Raymond G.
Johns Andrew W.
Nakhjavan Shervin K
Velez Lisa
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