Studies on tissue characterization by texture analysis with co-occurrence matrix method using ultrasonography and CT imaging

We used texture analysis with the co-occurrence matrix method to analyze ultrasonograms from normal and diseased livers, and X-ray CT images obtained from normal cases and cases of idiopathic interstitial pneumonia. Ten cases of normal, fatty, and cirrhotic livers; 10 cases of normal lungs; and 10 cases of idiopathic interstitial pneumonia, all confirmed by clinical findings, laboratory data, surgery, or biopsy, were the subjects of this study. We compared the results of texture analysis in normal and diseased livers under the same conditions of gain, focus, magnification rate, probe frequency, and depth of the region of interest. Here we discuss the relationship between Fisher ratio of texture analysis and pathological character. Although the normal and diseased liver groups did not differ significantly, the different pathological grades of fibrosis and the different size of nodules in the cirrhotic and normal liver groups did have different Fisher ratios. We compared the results of texture analysis with images obtained from normal cases and cases of idiopathic interstitial pneumonia. Significant differences between normal lungs and those with idiopathic interstitial pneumonia were also found. We thus think that texture analysis can be used to analyze ultrasonograms obtained from lesions of different pathological grades and to classify CT images as well.

[1]  M. Säbel,et al.  Recent developments in breast imaging. , 1996, Physics in medicine and biology.

[2]  C. R. Hill,et al.  Acoustic properties of normal and cancerous human liver-II. Dependence of tissue structure. , 1981, Ultrasound in medicine & biology.

[3]  R. L. Romijn,et al.  Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results. , 1991, Physics in medicine and biology.

[4]  T. Nash,et al.  Schistosome infections in humans: perspectives and recent findings. NIH conference. , 1982, Annals of internal medicine.

[5]  W. J. Lorenz,et al.  Diagnostic accuracy of computerized B‐scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease , 1985, Journal of clinical ultrasound : JCU.

[6]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  W. Vogel,et al.  [Sonography of the liver in Wilson's disease. Sonographic studies of the liver in Wilson's disease--significance for assessing prognosis?]. , 1988, Ultraschall in der Medizin.

[8]  G E Mailloux,et al.  Texture analysis of ultrasound B-mode images by segmentation. , 1984, Ultrasonic imaging.

[9]  A. H. Mir,et al.  Texture analysis of CT-images for early detection of liver malignancy. , 1995, Biomedical sciences instrumentation.

[10]  A Lorenz,et al.  Computerized Ultrasound B-Scan Texture Analysis of Experimental Fatty Liver Disease: Influence of Total Lipid Content and Fat Deposit Distribution , 1990, Ultrasonic imaging.

[11]  G. van Kaick,et al.  Quantification and classification of echographic findings in the thyroid gland by computerized B-mode texture analysis. , 1989, European journal of radiology.

[12]  K. Itoh,et al.  Studies on frequency‐dependent attenuation in the normal liver and spleen and in liver diseases, using the spectral‐shift zero‐crossing method , 1988, Journal of clinical ultrasound : JCU.

[13]  P. Scheuer,et al.  Liver Damage due to Methotrexate in Patients with Psoriasis , 1971, British medical journal.

[14]  J Grotepass,et al.  [Classification and texture attributed 3D reconstruction of breast tumors from ultrasound image sequences]. , 1989, Biomedizinische Technik. Biomedical engineering.

[15]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  G. van Kaick,et al.  Computerized ultrasound B‐scan texture analysis of experimental diffuse parenchymal liver disease: Correlation with histopathology and tissue composition , 1991, Journal of clinical ultrasound : JCU.

[17]  Qi Tian,et al.  Image Classification By The Foley-Sammon Transform , 1986 .

[18]  S. M. Collins,et al.  Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of experimental myocardial contusion. , 1983, Circulation.

[19]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[20]  R Collorec,et al.  [Analysis of texture in medical imaging. Review of the literature]. , 1995, Annales de radiologie.

[21]  B. Garra,et al.  Improving the Distinction between Benign and Malignant Breast Lesions: The Value of Sonographic Texture Analysis , 1993 .

[22]  G. Judmaier,et al.  Zur Sonographie der Leber bei Morbus Wilson , 2008 .

[23]  R. Haggitt,et al.  Pericentral hepatic fibrosis and intracellular hyalin in diabetes mellitus , 1980 .

[24]  A J Tajik,et al.  Feasibility of identifying amyloid and hypertrophic cardiomyopathy with the use of computerized quantitative texture analysis of clinical echocardiographic data. , 1989, Journal of the American College of Cardiology.

[25]  W. Schneider,et al.  Klassifikation und texturattributierte 3D-Rekonstruktion von Mammatumoren aus Ultraschallbildsequenzen , 1989 .

[26]  K. Itoh,et al.  Acoustic intensity histogram pattern diagnosis of liver diseases , 1985, Journal of clinical ultrasound : JCU.

[27]  E Picano,et al.  Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of myocardial amyloidosis. , 1989, Journal of the American College of Cardiology.

[28]  M. Meng,et al.  Intraoperative sonography for the evaluation and management of renal tumors: experience with 100 patients. , 1995, The Journal of urology.

[29]  G. Mailloux,et al.  Computer analysis of echographic textures in hashimoto disease of the thyroid , 1986, Journal of clinical ultrasound : JCU.

[30]  D T Morris The correction of ultrasonic image analysis features for their depth dependence. , 1988, International journal of bio-medical computing.