Digital Image Processing in Medical Applications, April 22, 2008

A number of methods for medical image analysis will be presented and their application to real cases will be discussed. In particular, attention will be focused on computer-aided detection (CAD) systems for lung nodule diagnosis in thorax-computed tomography (CT) and breast cancer detection in mammographic images. In the first case both a region growing (RG) algorithm for lung parenchymal tissue extraction and an active contour model (ACM) for anatomic lung contour detection will be described. In the second case, we will focus on a Haralik’s textural feature extraction scheme for the characterization of the regions of interest (ROIs) of the mammogram and a supervised neural network for the classification of the ROIs.

[1]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

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

[3]  L. Tabár,et al.  Potential contribution of computer-aided detection to the sensitivity of screening mammography. , 2000, Radiology.

[4]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[5]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[6]  Kin-Man Lam,et al.  An accurate active shape model for facial feature extraction , 2004, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004..

[7]  S. Tangaro,et al.  A novel Active Contour Model algorithm for contour detection in complex objects , 2007, 2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[8]  W. Heindel,et al.  Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting , 2001, European Radiology.

[9]  R. Bellotti,et al.  A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. , 2007, Medical physics.

[10]  W. Heindel,et al.  Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. , 2002, Radiology.

[11]  E. Thurfjell,et al.  Benefit of independent double reading in a population-based mammography screening program. , 1994, Radiology.

[12]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[13]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[14]  Margrit Betke,et al.  Chest CT: automated nodule detection and assessment of change over time--preliminary experience. , 2001, Radiology.

[15]  S. Armato,et al.  Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. , 2002, Radiology.

[16]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[17]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[18]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[19]  Dong Joong Kang,et al.  A fast and stable snake algorithm for medical images , 1999, Pattern Recognition Letters.

[20]  S Tangaro,et al.  A completely automated CAD system for mass detection in a large mammographic database. , 2006, Medical physics.

[21]  T Isomura,et al.  Lung cancer screening: minimum tube current required for helical CT. , 2000, Radiology.

[22]  Kai-Tai Song,et al.  Real-time image tracking for automatic traffic monitoring and enforcement applications , 2004, Image Vis. Comput..

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

[24]  L. Tabár,et al.  REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.

[25]  Anne-Marie Sykes,et al.  CT screening for lung cancer: five-year prospective experience. , 2005, Radiology.

[26]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[27]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[28]  C. Beam,et al.  Effect of human variability on independent double reading in screening mammography. , 1996, Academic radiology.

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