Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method.

RATIONALE AND OBJECTIVES The free-response paradigm is being increasingly used in the assessment of medical imaging systems. The currently implemented method of analyzing the data, namely jackknife free-response (JAFROC) analysis, has some validation and applicability limitations. The purpose of this work is to address these limitations. MATERIALS AND METHODS The general principles of modality evaluation and methodology validation are reviewed. A model for simulating free-response data was used to test the statistical validity of several methods of analyzing the data. The methods differed only in the choice of the figure of merit used to quantify performance. Statistical validity was judged by investigating the behaviors of the methods under null hypothesis conditions of no difference between modalities. RESULTS The validity of the different methods of analyzing the data was found to be dependent on the choice of figure of merit. A figure of merit is identified that accommodates abnormal images with multiple (one or more) lesions, detections of which could have different clinical significances (weights). This figure of merit is shown to be statistically valid. An extension of the analysis to single reader interpretations of images from different modalities is also shown to be statistically valid. CONCLUSION With the validated enhancements, JAFROC is expected to be of greater utility to users of the free-response method. The extension to single-reader interpretations should be of particular value to developers of image processing algorithms, including developers of computer-aided diagnosis algorithms.

[1]  C A Roe,et al.  Dorfman-Berbaum-Metz method for statistical analysis of multireader, multimodality receiver operating characteristic data: validation with computer simulation. , 1997, Academic radiology.

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

[3]  Baoyu Zheng,et al.  Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications , 1996, IEEE Trans. Medical Imaging.

[4]  Darrin C. Edwards,et al.  Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. , 2002, Medical physics.

[5]  Hans Bornefalk Estimation and comparison of CAD system performance in clinical settings. , 2005, Academic radiology.

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

[7]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[8]  Laurie L Fajardo,et al.  Free-response receiver operating characteristic evaluation of lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms. , 2005, Radiology.

[9]  B. J. Winer Statistical Principles in Experimental Design , 1992 .

[10]  R. F. Wagner,et al.  Assessment of medical imaging and computer-assist systems: lessons from recent experience. , 2002, Academic radiology.

[11]  David Gur,et al.  A comparison of two data analyses from two observer performance studies using Jackknife ROC and JAFROC. , 2005, Medical physics.

[12]  John F. Hamilton,et al.  A Free Response Approach To The Measurement And Characterization Of Radiographic Observer Performance , 1977, Other Conferences.

[13]  C E Metz,et al.  Variance-component modeling in the analysis of receiver operating characteristic index estimates. , 1997, Academic radiology.

[14]  Dev Chakraborty,et al.  Statistical power in observer-performance studies: comparison of the receiver operating characteristic and free-response methods in tasks involving localization. , 2002, Academic radiology.

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

[16]  D. Dorfman,et al.  Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data , 1969 .

[17]  C. Metz ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[18]  David Gur,et al.  A permutation test sensitive to differences in areas for comparing ROC curves from a paired design , 2005, Statistics in medicine.

[19]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[20]  Y H Chang,et al.  Computer-aided detection of clustered microcalcifications in digitized mammograms. , 1995, Academic radiology.

[21]  Anna Bornefalk Hermansson,et al.  On the comparison of FROC curves in mammography CAD systems. , 2005, Medical physics.

[22]  Frank W. Samuelson,et al.  Comparing image detection algorithms using resampling , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[23]  R. Larsen An introduction to mathematical statistics and its applications / Richard J. Larsen, Morris L. Marx , 1986 .