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Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images

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Abstract

To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively.

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References

  1. Gisvold JJ, Martin JJ: Prebiopsy localization of nonpalpable breast lesions. AJR 143: 477-481, 1984

    Google Scholar 

  2. Rosenberg L, Schwartz GF, Feig SA, Patchefsky AS: Clinical occult breast lesions: Localization and significance. Radiology 162: 167-170, 1987

    Google Scholar 

  3. Bassett LW, Liu TH, Giuliano AI, Gold RH: The prevalence of carcinoma in palpable versus impalpable, mammographically detected lesions (comment). ARJ 157: 21-24, 1991

    Google Scholar 

  4. Mark R, Kent H, Nancy J, William H, Robert R, Erick R, Michael F: Use of fine needle aspiration for solid breast lesions is accurate and cost-effective. Am J Surg 174: 694-698, 1997

    Google Scholar 

  5. Gilles R, Guinebretiere JM, Lucidarme O, Cluzel P, Janaud G, Finet JF, Tardivon A, Masselot J, Vanel D: Nonpalpable breast tumors: Diagnosis with contrast-enhanced subtraction dynamic MR imaging. Radiology 191: 625-631, 1994

    Google Scholar 

  6. Stomper PC, Herman S, Klippenstein DL, Winston JS, Edge SB, Arredondo MA, Mazurchuk RV, Blumenson LE: Suspect breast lesions: Finding at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features. Radiology 197: 387-395, 1995

    Google Scholar 

  7. Palmedo H, Grunwald F, Bender H, Schomburg A, Mallmann P, Krebs D, Biersack HJ: Scintimammography with technetium-99m methoxyisobutylisonitrile: Comparison with mammography and magnetic resonance imaging. Eur J Nucl Med 23: 940-946, 1996

    Google Scholar 

  8. Tiling R, Sommer H, Pechmann M, Moser R, Kress K, Pfluger T, Tatsch K, Hahn K: Comparision of technetium-99m-sestamibi scintimammography with contrast-enhanced MRI for diagnosis of breast lesions. J Nucl Med 38: 58-62, 1997

    Google Scholar 

  9. Petrick N, Chan HP, Sahiner B, Wei D: An adaptive densityweighed contrast enhancement filter for mammographic breast mass dection. IEEE Trans Med Imag 15: 59-67, 1996

    Google Scholar 

  10. Dhawan AP, Chitre Y, Kaiser-Bnoasso C, Moskowitz M: Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans Med Imag 15: 246-259, 1996

    Google Scholar 

  11. Zheng B, Qian W, Clarke LP: Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications. IEEE Trans Med Imag 15: 589-597, 1996

    Google Scholar 

  12. Sahiner B, Chan HP, Petrick N, Wei D, Helvi MA, Adler DD, Goodsitt MM: Classification of mass and normal breast tissue: A convolution neural network classifier with spatial doma in and texture images. IEEE Trans Med Imag 15: 598-610, 1996

    Google Scholar 

  13. Shankar PM, Reid JM, Ortega H, Piccoli CW, Goldberg BB: Use of non-Rayleigh statistics for identification of tumors in ultrasonic B-scans of the breast. IEEE Trans Med Imag 12: 687-692, 1993

    Google Scholar 

  14. Stavros T, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA: Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions. Radiology 196: 123-134, 1995

    Google Scholar 

  15. Chen MS, Han JW, Yu PS: Data mining: An overview from a database perspective. IEEE Trans Knowledge Data Eng 8: 866-883, 1996

    Google Scholar 

  16. Mullich J: Data mining: making data meaningful. Computer 30: 18, 1997

    Google Scholar 

  17. Anand SS, Scotney BW, Tan MG, Mcclean SI, Bell DA, Hughes JG, Magill IC: Designing a kernel for data mining. IEEE Expert [see also IEEE Intelligent Systems] 12: 65-74, 1997

    Google Scholar 

  18. Cios KJ, Pedrycz W, Swiniarsk RM: Data mining methods for knowledge discovery. IEEE Trans Neural Networks 9: 1533-1534, 1998

    Google Scholar 

  19. Valckx FMJ, Thijssen JM: Characterization of echographic image texture by co-occurrence matrix parameters. Ultrasound Med Biol 23: 559-571, 1997

    Google Scholar 

  20. Vittitoe NF, Baker JA, Floyd CE Jr: Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules. Acad Radiol 4: 96-101, 1997

    Google Scholar 

  21. Petrick N, Chan HP, Wei D, Sahiner B, Helvie MA, Adler DD: Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. Med Phys 23: 1685-1696, 1996

    Google Scholar 

  22. McPherson DD, Aylward PE, Knosp BM, Bean JA, Kerber RE, Collins SM, Skorton DJ: Ultrasound characterization of acute myocardial ischemia by quantitative texture analysis. Ultrasonic Imaging 8: 227-240, 1986

    Google Scholar 

  23. Layer G, Zuna I, Lorenz A, Zerban H, Haberkorn U, Bannasch P, Van Kaick G, Rath U: Computerized ultrasound B-scan texture analysis of experimental diffuse parenchymal liver disease: Correlation with histopathology and tissue composition. J Clin Ultrasound 19: 193-201, 1991

    Google Scholar 

  24. Garra BS, Krasner BH, Horii SC, Ascher S, Mun SK, Zeman RK: Improving the distinction between benign and malignant breast lesions: The value of sonographic texture analysis. Ultrasonic Imaging 15: 267-285, 1993

    Google Scholar 

  25. Unser M: Sum and difference histograms for texture classification. IEEE Trans Pattern Analys Mach Intellig PAMI-8: 118, 1986

    Google Scholar 

  26. Roux C, Coatrieux JL: Contemporary perspectives in threedimensional biomedical imaging. Amsterdam, The Netherlands: IOS Press, 1997, p. 141

    Google Scholar 

  27. Hu R, Fahmy MM: Texture segmentation based on a hierarchical Markov random field model. Signal Processing 26: 285-305, 1992

    Google Scholar 

  28. Derin H, Elliot H: Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans Pattern Analys Mach Intellig 9: 39-55, 1987

    Google Scholar 

  29. Chen CC, Daponte JS, Fox MD: Fractal feature analysis and classification in medical imaging. IEEE Trans Med Imag 8: 133-142, 1989

    Google Scholar 

  30. Pentland A: Fractal-based description of natural scenes. IEEE Trans Pattern Analys Mach Intellig 6: 661-674, 1984

    Google Scholar 

  31. Quinlan JR: C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo, California, 1992

    Google Scholar 

  32. Goldberg V, Maduca A, Ewert DL, Gisvold JJ, Gerrnleaf JF: Improvement specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence. Med Phys 19: 1475-1481, 1992

    Google Scholar 

  33. Burbank F.: Stereotatic breast biopsy: Its history, its present, and its future. Am Surg 62: 128-150, 1996

    Google Scholar 

  34. Morrow M: When can stereotatic core biopsy replace excisional biopsy?-A clinical perspective. Breast Cancer Res Treat 36: 1-9, 1995

    Google Scholar 

  35. Hall FM, Storella JM, Silverstone DZ, Wyshak G: Nonpalpable breast lesions: recommendations for biopsy based on suspecion of carcinoma at mammography. Radiology 167: 353-358, 1988

    Google Scholar 

  36. Citto S, Cataliotti L, Distante V: Nonpalpable lesions dected with mammography: review of 512 consecutive cases. Radiology 165: 99-102, 1987

    Google Scholar 

  37. Raza S, Baum JK: Solid breast lesions: evaluation with power doppler US. Radiology 203: 164-168, 1997

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

    Wen-Jia Kuo & Ruey-Feng Chang

  2. Department of General Surgery, China Medical College & Hospital Taichung, Taiwan

    Dar-Ren Chen

  3. Department of Medical Research, China Medical College & Hospital Taichung, Taiwan

    Cheng Chun Lee

Authors
  1. Wen-Jia Kuo
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  2. Ruey-Feng Chang
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  3. Dar-Ren Chen
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  4. Cheng Chun Lee
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Kuo, WJ., Chang, RF., Chen, DR. et al. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Res Treat 66, 51–57 (2001). https://doi.org/10.1023/A:1010676701382

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