A quantitative method for evaluating the detectability of lesions in digital mammography.

This study presents a quantitative method for evaluating the detectability of microcalcifications in digital mammography. Four hundred and twenty microcalcifications (with various morphology, size and contrast), simulated with a previously validated method, were used for the creation of image datasets. Lesions were inserted into 163 regions of interests of 59 selected raw digital mammograms with various anatomical backgrounds and acquired with a Siemens Novation DR. After processing, these composite images were scored by experienced radiologists, who located multiple simulated lesions and rated them under conditions of free-search. For statistical analysis, free-response receiver-operating characteristic curves are plotted; the use of jackknife free-response receiver-operating characteristic method has also been investigated. The main advantage of this methodology is that the exact number of inserted microcalcifications is well known and that the lesions are fully characterised in terms of pathology, size, morphology and peak contrast. A first application has been the evaluation of the effect of anatomical background on microcalcifications detection. Preliminary findings in this study indicate that this method may be a promising tool to evaluate factors that have an influence on the detectability of lesions, such as the clinical processing or the viewing conditions.

[1]  Andrew D. A. Maidment,et al.  Image processing algorithms for digital mammography: a pictorial essay. , 2000, Radiographics : a review publication of the Radiological Society of North America, Inc.

[2]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[3]  Dev P Chakraborty,et al.  Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. , 2006, Academic radiology.

[4]  A H Baydush,et al.  Improved image quality in digital mammography with image processing. , 2000, Medical physics.

[5]  K. Muller,et al.  Improving the detection of simulated masses in mammograms through two different image-processing techniques. , 2001, Academic radiology.

[6]  Dev P. Chakraborty,et al.  Jackknife free-response ROC methodology , 2004, SPIE Medical Imaging.

[7]  M Le Gal,et al.  [Diagnostic value of clustered microcalcifications discovered by mammography (apropos of 227 cases with histological verification and without a palpable breast tumor)]. , 1984, Bulletin du cancer.

[8]  N. Obuchowski,et al.  Comparing the performance of mammographic enhancement algorithms: a preference study. , 2000, AJR. American journal of roentgenology.

[9]  D P Chakraborty,et al.  Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data. , 1989, Medical physics.

[10]  Development and validation of a simulation procedure to study the visibility of micro calcifications in digital mammograms. , 2003, Medical physics.

[11]  Donglin Zeng,et al.  The effects of gray scale image processing on digital mammography interpretation performance. , 2005, Academic radiology.

[12]  Hilde Bosmans,et al.  Evaluation of the global effect of anatomical background on microcalcifications detection , 2007, SPIE Medical Imaging.

[13]  Ann-Katherine Carton,et al.  Quantification of Al-equivalent thickness of just visible microcalcifications in full field digital mammograms. , 2004, Medical physics.

[14]  Dev P Chakraborty,et al.  Observer studies involving detection and localization: modeling, analysis, and validation. , 2004, Medical physics.

[15]  Hilde Bosmans,et al.  Development and implementation of a user friendly and automated environment for the creation of databases of digital mammograms with simulated microcalcifications , 2006, SPIE Medical Imaging.

[16]  Bradley M. Hemminger,et al.  The effect of intensity windowing on the detection of simulated masses embedded in dense portions of digitized mammograms in a laboratory setting , 2009, Journal of Digital Imaging.

[17]  W. Huda,et al.  Mammographic image processing using wavelet processing techniques , 1995, European Radiology.

[18]  B. Efron.  The jackknife, the bootstrap, and other resampling plans , 1987 .

[19]  Robert M. Nishikawa,et al.  Radiologists’ Preferences for Digital Mammographic Display , 2000 .

[20]  Lena Costaridou,et al.  A digital equalisation technique improving visualisation of dense mammary gland and breast periphery in mammography. , 2003, European journal of radiology.