Automatic recognition of spicules in mammograms

  • Hao Jiang
  • Wilson Tiu
  • Shinji Yamamoto
  • Shun-ichi Iisaku
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

This paper presents a method of automatic recognition of spicules in mammograms. The method is consisted of two steps, enhancement and feature selection. First, spicule shadows are enhanced by using a newly developed operation. An opening operation is applied to remove noises and a direction map is made for feature selection. Second, a concentration expression is given with gray levels and two features are selected for recognition of tumors with spicules. In the method, the direction of spicules is not only considered, but the density is also utilized. The method was tested on 24 samples including seven tumors with spicules. The recognition rate for tumors with spicules was 100% without the false positives.

Keywords

Feature Selection Original Image Gray Level Compute Radiography Seed Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    W.Spiesberger: Mammogram inspection by computer, IEEE Trans. Biomed. Engin., Vol.BME-26, No.4, pp.213–219, 1979Google Scholar
  2. [2]
    A.P.Dhawan, G.Buelloni and R.Gordon: Enhancement of mammographic features by optimal adaptive neighborhood image processing, IEEE Trans. Med. Imaging, Vol.MI-5, No. 1, pp.8–15, 1986Google Scholar
  3. [3]
    S.-M.Lai, X.Li and W.F.Bischof: On techniques for detecting circumscribed masses in mammograms, IEEE Trans. Med. Imaging, Vol.MI-8, No.4, pp.377–386, 1998Google Scholar
  4. [4]
    D.Brzakovic, X.M.Luo and P.Brzakovic: An approach to automated detection of tumors in mammograms, IEEE Trans. Med. Imaging, Vol.MI-9, No.3, pp.233–241, 1990CrossRefGoogle Scholar
  5. [5]
    W.M.Morrow, R.B.Paranjape, R.M.Rangayyan and J.E.L.Desautels: Region-based contrast enhancement of mammograms, IEEE Trans. Med. Imaging, Vol.MI-11, No.3, pp.392–406, 1992CrossRefGoogle Scholar
  6. [6]
    J.Dengler, S.Bebine and J.F.Desaga: Segmentation of microcalcifications in mammograms, IEEE Trans. Med. Imaging, Vol.MI-12, No.4, pp.634–642, 1990Google Scholar
  7. [7]
    S.L.Ng and W.B.Bischof: Automated detection and classification of breast tumors. Computers and Biomedical Research, Vol.25, pp.218–237, 1992CrossRefPubMedGoogle Scholar
  8. [8]
    W.P.Kegelmeyer, Jr.: Computer detection of stellate lesions in mammograms, Proc.'92 SPIE Conf. on Biomedical Image Processing and 3-D Microscopy, Vol.1660, pp.446–454, 1992Google Scholar
  9. [9]
    W.P.Kegelmeyer, Jr.: Evalution of stellate lesion detection in a standard mammogram data set, in K.W.Bowyer and S.Astley eds: State of the art in digital mammographic image analysis, World Sientific, pp.262–279, 1993Google Scholar
  10. [10]
    W.Qian, L.P.Clarke, M.Kallergi and R.A.Clark: Tree-structured nonlinear filters in digital mammography, IEEE Trans. Med. Imaging, Vol.MI-13, No. 1, pp.25–36, 1994CrossRefGoogle Scholar
  11. [11]
    L.Shen, R.M.Rangayyan and J.E.L.Desautels: Application of shape analysis to mammographic calcifications, IEEE Trans. Med. Imaging, Vol.MI-13, No.2, pp.263–274, 1994Google Scholar
  12. [12]
    R.P.Highnam, J.M.Brady and B.J.Shepstone: Computing the scatter component of mammographic images, IEEE Trans. Med. Imaging, Vol.MI-13, No.2, pp.301–313, 1994CrossRefGoogle Scholar
  13. [13]
    A.F.Laine, S.Schuler, J.Fan and W.Huda: Mammographic feature enhancement by multiscale analysis, IEEE Trans. Med. Imaging, Vol.MI-13, No.4, pp.725–740, 1994CrossRefGoogle Scholar
  14. [14]
    N.Petrick, H.-P.Chan, B.Sahiner and D.Wei: An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection, IEEE Trans. Med. Imaging, Vol.MI-15, No. 1, pp.59–67, 1996CrossRefGoogle Scholar
  15. [15]
    R.N.Strickland and H.I.Hahn: Wavelet transforms for detecting microcalcifications in mammograms, IEEE Trans. Med. Imaging, Vol.MI-15, No.2, pp.218–229, 1996CrossRefGoogle Scholar
  16. [16]
    A.P.Dhawan, Y.Chitre, C.Kaiser-Bonasso and M.Moskowitz: Analysis of mammographic microcalcifications using gray-level image structure features, IEEE Trans. Med. Imaging, Vol.MI-15, No.3, pp.246–259, 1996CrossRefGoogle Scholar
  17. [17]
    B.Zheng, W.Qian and L.P.Clarke: Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications, IEEE Trans. Med. Imaging, Vol.MI-15, No.5, pp.589–597, 1996CrossRefGoogle Scholar
  18. [18]
    B.Sahiner, H.-P.Chan, N.Petrick, D.Wei, M.A.Helvie, D.D.Adler and M.M.Goodsitt: Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images, IEEE Trans. Med. Imaging, Vol.MI-15, No.5, pp.598–610, 1996CrossRefGoogle Scholar
  19. [19]
    H.Kobatake and Y. Yoshinaga: Detection of spicules on mammograms based on skeleton analysis, IEEE Trans. Med. Imaging, Vol.MI-15, No.3, pp.235–245, 1996CrossRefGoogle Scholar
  20. [20]
    N.Karssemeijer G.M.te Brake: Detection of stellate distortion in mammograms, IEEE Trans. Med. Imaging, Vol.MI-I5, No. 5, pp.611–619, 1996CrossRefGoogle Scholar
  21. [21]
    S.Yamamoto, M.Matsumoto, Y.Tateno, T.Iinuma and T.Matsumoto: Quoit filter — a new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray CT, Proc. of 13th ICPR, Vol.II, pp.3–7, 1996Google Scholar
  22. [22]
    L. Vincent: Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms, IEEE Trans. Image Processing, Vol.IP-2, No.2, pp. 176–201, 1993CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Hao Jiang
    • 1
  • Wilson Tiu
    • 2
  • Shinji Yamamoto
    • 2
  • Shun-ichi Iisaku
    • 1
  1. 1.Communications Research Laboratory, MPTKoganei, TokyoJapan
  2. 2.Toyohashi University of TechnologyToyohashi, AichiJapan

Personalised recommendations