Skip to main content

The Computer-Aided Diagnosis of Soft-Tissue Tumors Using Sonographic Morphology and Texture Features

  • Chapter
  • First Online:
Dermatologic Ultrasound with Clinical and Histologic Correlations

Abstract

Computer-aided diagnosis (CAD) is based on shape and echogenicity of tumors and is used to extract relevant information. The various steps of the general CAD algorithm for tumor diagnosis are discussed in this chapter. A CAD system that integrates these features has been proved to be capable of assisting radiologists in the diagnosis of soft-tissue tumors on sonography. Thus, in the characterization of benign and malignant soft-tissue tumors, the CAD system successfully implements the classification using two types of feature sets, including morphological and texture features. Notably, the performance of the morphological features seems to be more relevant in the determination of the tumor status. Nevertheless, both morphological and texture features remain the most important properties while visually interpreting soft-tissue tumors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chiou HJ, Chou YH, Chiou SY, Wang HK. High-resolution ultrasonography in superficial soft tissue tumors. J Med Ultrasound. 2007;15:152–74.

    Article  Google Scholar 

  2. Gandhi MR, Benson MD. Ultrasound of soft tissue masses. World J Surg. 2000;24:227–31.

    Article  PubMed  CAS  Google Scholar 

  3. Garcia-Gomez JM, Vidal C, Marti-Bonmati DL, Galant J, Sans N, Robles M, et al. Benign/malignant classifier of soft tissue tumors using MR imaging. MAGMA. 2004;16:194–201.

    Article  PubMed  Google Scholar 

  4. Verstraete KL, Vanzieleghem B, De Deene Y, Palmans H, De Greef D, Kristoffersen DT, et al. Static, dynamic and first-pass MR imaging of musculoskeletal lesions using gadodiamide injection. Acta Radiol. 1995;36:27–36.

    PubMed  CAS  Google Scholar 

  5. Clark MA, Fisher C, Judson I, Thomas JM. Soft-tissue sarcomas in adults. N Engl J Med. 2005;353:701–11.

    Article  PubMed  CAS  Google Scholar 

  6. Morton L, Antman KH, Tepper J. Soft tissue sarcomas cancer medicine. 4th ed. Philadelphia: Williams & Wilkins; 1997. p. 2559–92.

    Google Scholar 

  7. Horsch K, Giger ML, Venta LA, Vyborny CJ. Computerized diagnosis of breast lesions on ultrasound. Med Phys. 2002;29:157–64.

    Article  PubMed  Google Scholar 

  8. Hadjiiski L, Chan HP, Sahiner B, Helvie MA, Roubidoux MA, Blane C, et al. Improvement in radiologists characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study1. Radiology. 2004;233:255–65.

    Article  PubMed  Google Scholar 

  9. McNally EG. Practical musculoskeletal ultrasound. Philadelphia, PA: Elsevier Churchill Livingstone; 2005.

    Google Scholar 

  10. Fisher C. Soft tissue sarcomas: diagnosis, classification and prognostic factors arthroscopy. J Arthrosc Relat Surg. 1996;49:27–33.

    CAS  Google Scholar 

  11. Adler RS. Musculoskeletal system ultrasound in medicine. Ultrasound Med Biol. 2000;26–27:S125.

    Article  Google Scholar 

  12. Chou YH, Tiu CM, Hung GS, Wu SC, Chang TY, Chiang HK. Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. Ultrasound Med Biol. 2001;27:1493–8.

    Article  PubMed  CAS  Google Scholar 

  13. Bodner G, Schocke MF, Rachbauer F, Seppi K, Peer S, Fierlinger A, et al. Differentiation of malignant and benign musculoskeletal tumors: combined color and power doppler US and spectral wave analysis. Radiology. 2002;223:410–6.

    Article  PubMed  Google Scholar 

  14. Olsson H. An updated review of the epidemiology of soft tissue sarcoma. Acta Orthop Scand Suppl. 2004;75:16–20.

    PubMed  CAS  Google Scholar 

  15. Skaane P, Engedal K. Analysis of sonographic features in the differentiation of fibroadenoma and invasive ductal carcinoma. AJR Am J Roentgenol. 1998;170:109–14.

    Article  PubMed  CAS  Google Scholar 

  16. Stavros AT, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology. 1995;196:123–34.

    PubMed  CAS  Google Scholar 

  17. Sintzoff SA, Gillard I, Van Gansbeke D, Gevenois PA, Salmon I, Struyven J. Ultrasound evaluation of soft tissue tumors. J Belge Radiol. 1992;75:276–80.

    PubMed  Google Scholar 

  18. Chen CM, Chou YH, Han KC, Hung GS, Tiu CM, Chiou HJ, et al. Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology. 2003;226:504.

    Article  PubMed  Google Scholar 

  19. Chang RF, Wu WJ, Moon WK, Chen DR. Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat. 2005;89:179–85.

    Article  PubMed  Google Scholar 

  20. Wu WJ, Moon WK. Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Acad Radiol. 2008;15:873–80.

    Article  PubMed  Google Scholar 

  21. Pau LF, Wang PSP. Handbook of pattern recognition and computer vision. Singapore: World Scientific Publishing Company; 1999.

    Book  Google Scholar 

  22. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. Syst Man Cy IEEE Trans. 1973;3:610–21.

    Article  Google Scholar 

  23. Van Gool L, Dewaele P, Oosterlinck A. Texture analysis anno 1983. Comp Vision Graph Image Process. 1985;29:336–57.

    Article  Google Scholar 

  24. Milan S, Vaclav H, Roger B. Image processing analysis and machine vision. Peking: Photocopy Edition Posts & Telecom Press; 2002.

    Google Scholar 

  25. Tuceryan M, Jain AK. Texture analysis, handbook of pattern recognition & computer vision. River Edge: World Scientific Publishing Co., Inc.; 1993.

    Google Scholar 

  26. Chellappa R, Chatterjee S. Classification of textures using gaussian markov random fields. IEEE Trans Acoust Speech Signal Proc. 1985;33:959–63.

    Article  Google Scholar 

  27. Teuner A, Pichler O, Hosticka BJ. Unsupervised texture segmentation of images using tuned matched gabor filters. IEEE Trans Image Process. 1995;4:863–70.

    Article  PubMed  CAS  Google Scholar 

  28. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Anal Mach Intell IEEE Trans. 1989;11:674–93.

    Article  Google Scholar 

  29. Laws KI. Texture energy measures. 1979.

    Google Scholar 

  30. Lefebvre F, Meunier M, Thibault F, Laugier P, Berger G. Computerized ultrasound B-scan characterization of breast nodules. Ultrasound Med Biol. 2000;26:1421–8.

    Article  PubMed  CAS  Google Scholar 

  31. Chen DR, Chang RF, Kuo WJ, Chen MC, Huang Y. Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol. 2002;28:1301–10.

    Article  PubMed  Google Scholar 

  32. Zheng K, Wang T, Lin J, Li D. Recognition of breast ultrasound images using a hybrid method. In: Complex Medical Engineering, 2007. CME 2007. IEEE/ICME international conference on, Beijing; 2007, pp 640–643.

    Google Scholar 

  33. Alvarenga AV, Pereira WC, Infantosi AF, Azevedo de CM. Classification of breast tumours on ultrasound images using morphometric parameters. Intelligent Signal Processing, 2005 IEEE international workshop on, Portugal: Coimbra University; 2005, pp 206–210.

    Google Scholar 

  34. Joo S, Yang YS, Moon WK, Kim HC. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans Med Imaging. 2004;23:1292–300.

    Article  PubMed  Google Scholar 

  35. Chang RF, Wu WJ, Moon WK, Chen DR. Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol. 2003;29:679–86.

    Article  PubMed  Google Scholar 

  36. Huang YL, Chen DR. Support vector machines in sonography: application to decision making in the diagnosis of breast cancer. Clin Imaging. 2005;29:179–84.

    Article  PubMed  Google Scholar 

  37. Piliouras N, Kalatzis I, Dimitropoulos N, Cavouras D. Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Comput Med Imaging Graph. 2004;28:247–55.

    Article  PubMed  CAS  Google Scholar 

  38. Rodrigues PS, Giraldi GA, Provenzano M, Faria MD, Chang RF, Suri JS. A new methodology based on q-entropy for breast lesion classification in 3-D ultrasound images. In: Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference on, New York; 2006, pp 1048–1051.

    Google Scholar 

  39. Jain AK, Duin RP, Mao J. Statistical pattern recognition: a review. Pattern Anal and Mach Intell IEEE Trans. 2000;22:4–37.

    Article  Google Scholar 

  40. Stone M. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B (Methodol). 1974;36:111–47.

    Google Scholar 

  41. Chen CY, Chiou HJ, Chou YH, Chiou SY, Wang HK, Chou SY, et al. Computer-aided diagnosis of soft tissue tumors on high-resolution ultrasonography with geometrical and morphological features. Acad Radiol. 2009;16:618–26.

    Article  PubMed  Google Scholar 

  42. Chen CY, Chiou HJ, Chou SY, Chiou SY, Wang HK, Chou YH, et al. Computer-aided diagnosis of soft-tissue tumors using sonographic morphologic and texture features. Acad Radiol. 2009;16:1531–8.

    Article  PubMed  Google Scholar 

  43. Kransdorf MJ, Jelinek JS, Moser Jr RP. Imaging of soft tissue tumors. Radiol Clin North Am. 1993;31:359–72.

    PubMed  CAS  Google Scholar 

  44. Widmann G, Riedl A, Schoepf D, Glodny B, Peer S, Gruber H. State-of-the-art HR-US imaging findings of the most frequent musculoskeletal soft-tissue tumors. Skeletal Radiol. 2009;38:637–49.

    Article  PubMed  Google Scholar 

  45. Tierney JF, Stewart LA, Parmar MKB. Adjuvant chemotherapy for localised resectable soft-tissue sarcoma of adults: meta-analysis of individual data. Lancet. 1997;350:1647–54.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong-Jen Chiou MD .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Chiou, HJ., Chen, CY., Chou, YH., Chiang, H.K. (2013). The Computer-Aided Diagnosis of Soft-Tissue Tumors Using Sonographic Morphology and Texture Features. In: Wortsman, X. (eds) Dermatologic Ultrasound with Clinical and Histologic Correlations. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7184-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7184-4_6

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7183-7

  • Online ISBN: 978-1-4614-7184-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics