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Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

By Ikhlas Abdel-Qader, Lixin Shen, Christina Jacobs, Fadi Abu Amara and Sarah Pashaie-Rad


Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database

Topics: Article
Publisher: Hindawi Publishing Corporation
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Provided by: PubMed Central

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  1. (1994). [1] C.C.Bo ring,T .Sq uir es,T .T o ng,andS.M o ntg o me ry
  2. (1994). [19] S.ZhanandR.Mehrotra,“Zero-crossing-basedoptimalthreedimensional edge detector,” Computer Vision, Graphics, and Image Processing: Image Understanding,
  3. (1997). [4] H.-P.Chan,B.Sahiner,N.Patrick,etal.,“Computerizedclassificationofmalignantandbenignmicrocalcificationsonmammograms: texture analysis using an artificial neural network,”
  4. (1996). A computational approach to zero-crossing-based two-dimensional edge detection,”
  5. (1995). A near patternmatching scheme based upon principal component analysis,”
  6. (1994). Application of shape analysis to mammographic calcifications,”
  7. (1995). Benefit of mammography screening in women ages 40 to 49 years: current evidence from randomized controlled trials,”
  8. (1986). ChristinaJacobs received her B.S. degree in neuroscience from University of Rochester in Rochestor,
  9. (2002). Comparison of feature extraction and selection methods in mammogram recognition,”
  10. (1999). Constrained hough transforms for curve detection,”
  11. (2001). degrees from Peking University, China, in 1987 and 1990, respectively, and his Ph.D. degree from Zhongshan University, China, in 1996, all in mathematics. From September1996toJuly2001,hewasaResearchFellow at
  12. (1998). Detection of microcalcifi-cations in digital mammograms using wavelets,”
  13. (2000). Directional 3D edge detection in anisotropic data: detector design and performance assessment,”
  14. Edge detection based on dynamic morphology,”
  15. (1995). Edge detection by curve fitting,”
  16. (1994). Edge detection using two-dimensional local structure information,”
  17. (2003). Feature extraction using pca and cluster analysis,”
  18. (2002). he was with the Department of Mathematics, West Virginia University. He is currently an Assistant Professor at the
  19. (2003). Highly regular wavelets for the detection of clustered microcalcifications in mammograms,”
  20. (2005). Image edge detection based on omnidirectional multiscale morphology,”
  21. (1987). Integrated directional derivative gradient operator,”
  22. (1962). Methods and means for recognizing complex patterns,” US patent no. 069654,
  23. (1992). o r r o w ,R .B .P a r a n j a p e ,R .M .R a n g a y y a n ,a n dJ
  24. (2004). Principal component analysis approach for biomedical sample identification,” in
  25. (1997). Principal component analysis of speech spectrogram images,”
  26. (1999). Separating background texture and image structure in mammograms,”
  27. (1972). Use of the hough transformation to detectlines andcurves inpictures,”Communications ofthe
  28. (1993). Which hough transform?” Computer Vision, Graphics, and Image Processing: Image Understanding,

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