Automated Detection of Breast Mass Spiculation Levels and Evaluation of Scheme Performance 1

RATIONALE AND OBJECTIVES Although the spiculation levels of breast mass boundaries are a primary sign of malignancy for masses detected on mammography, developing an automated computerized method to detect spiculation levels and quantitatively evaluating the performance of such a method is a difficult task. The objectives of this study were to (1) develop and test a new method to improve mass segmentation and detect mass boundary spiculation levels and (2) assess the performance of this method using a relatively large imaging data set. MATERIALS AND METHODS The fully automated method developed for this study includes three image-processing steps. In the first step, the principle of maximum entropy is applied in the selected region of interest (ROI) after correcting the background trend to enhance the initial outlines of a mass. In the second step, an active-contour model is used to refine the initial outlines. In the third step, spiculated lines connected to the mass boundary are detected and identified using a special line detector. A quantitative spiculation index is computed to assess the degree of spiculation. To develop and evaluate this automated method, 211 ROIs depicting masses were extracted from a publicly available image database. Among these ROIs, 106 depicted circumscribed mass regions and 105 involved spiculated mass regions. The performance of the method was evaluated using receiver-operating characteristic (ROC) analysis. RESULTS The computed area under the ROC curve, when applying the method to the data set, was 0.701 +/- 0.027. By setting up a threshold at a spiculation index of 5.0, the method achieved an overall classification accuracy of 66.4%, with 54.3% sensitivity and 78.3% specificity. CONCLUSIONS In this study, a new computerized method with a number of unique characteristics was developed to detect spiculated mass regions, and a simple spiculation index was applied to quantify mass spiculation levels. Although this quantitative index can be used to distinguish between spiculated and circumscribed masses, the results also suggest that the automated detection of mass spiculation levels remains a technical challenge.

[1]  R Rusting,et al.  Making headway against cancer. , 1996, Scientific American.

[2]  Nico Karssemeijer,et al.  Detection of stellate distortions in mammograms , 1996, IEEE Trans. Medical Imaging.

[3]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[4]  N. Karssemeijer,et al.  Segmentation of suspicious densities in digital mammograms. , 2001, Medical physics.

[5]  C. Vyborny,et al.  Breast cancer: importance of spiculation in computer-aided detection. , 2000, Radiology.

[6]  M. Turco,et al.  Breast cancer update: Encouraging trends…. Many new questions , 1999 .

[7]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[8]  Georgia D. Tourassi,et al.  A Concentric Morphology Model for the Detection of Masses in Mammography , 2007, IEEE Transactions on Medical Imaging.

[9]  Lubomir M. Hadjiiski,et al.  Improvement of mammographic mass characterization using spiculation meausures and morphological features. , 2001, Medical physics.

[10]  J. M. Pruneda,et al.  Computer-aided mammographic screening for spiculated lesions. , 1994, Radiology.

[11]  H P Chan,et al.  Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. , 1996, Medical physics.

[12]  N. Karssemeijer,et al.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. , 2004, Medical physics.

[13]  L. Tabár,et al.  Beyond randomized controlled trials , 2001, Cancer.

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

[15]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[16]  David M Catarious,et al.  Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. , 2004, Medical physics.

[17]  R. Smith,et al.  Breast cancer screening among women younger than age 50: A current assessment of the issues , 2000, CA: a cancer journal for clinicians.

[18]  R A Clark,et al.  False-positive reduction in CAD mass detection using a competitive classification strategy. , 2001, Medical physics.

[19]  Martin P DeSimio,et al.  A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size. , 2005, AJR. American journal of roentgenology.

[20]  T. Freer,et al.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. , 2001, Radiology.

[21]  Claudia Mello-Thoms,et al.  Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library. , 2007, Academic radiology.

[22]  David Gur,et al.  Mass margins spiculations: agreement between ratings by observers and a computer scheme , 2007, SPIE Medical Imaging.

[23]  Chih-Cheng Hung,et al.  Mass edge detection in mammography based on plane fitting and dynamic programming , 2007, SAC '07.

[24]  C. Mettlin,et al.  Global breast cancer mortality statistics , 1999, CA: a cancer journal for clinicians.

[25]  Y H Chang,et al.  Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. , 1995, Academic radiology.

[26]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .

[27]  P. Taylor,et al.  Computer-aided detection in the United Kingdom National Breast Screening Programme: prospective study. , 2005, Radiology.

[28]  M. Giger,et al.  Analysis of spiculation in the computerized classification of mammographic masses. , 1995, Medical physics.

[29]  David Gur,et al.  Computer-aided detection performance in mammographic examination of masses: assessment. , 2004, Radiology.

[30]  A B Miller.  Mammography: reviewing the evidence. Epidemiology aspect. , 1993, Canadian family physician Medecin de famille canadien.

[31]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[32]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[33]  David Gur,et al.  A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. , 2005, Medical physics.

[34]  J. A. Campbell.  ACADEMIC RADIOLOGY. , 1965, The American journal of roentgenology, radium therapy, and nuclear medicine.

[35]  Kenji Suzuki,et al.  A dual-stage method for lesion segmentation on digital mammograms. , 2007, Medical physics.

[36]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[37]  Robert M. Nishikawa,et al.  Current status and future directions of computer-aided diagnosis in mammography , 2007, Comput. Medical Imaging Graph..