Computer-aided detection of clustered microcalcifications on digitized mammograms: a robustness experiment.

RATIONALE AND OBJECTIVES The authors assessed the performance of an existing computer-aided diagnosis (CAD) scheme for the detection of clustered microcalcifications in a large image database. METHODS A previously developed, rule-based system was used to assess detectability of microcalcification clusters in a set of 386 digitized mammograms with 239 verified clusters visible on 191 images. The test was performed without any reoptimization of the scheme. None of the 386 images had been used in any previous scheme development or testing procedures. RESULTS The CAD scheme achieved 89.5% sensitivity at an average false-positive detection rate of 0.39 per image. In 75% of all images, no false-positive findings occurred. Twenty-three of 25 false-negative findings (misses) occurred during the last two stages in the detection process. CONCLUSION This scheme produced reasonable results in a large data set of images with a large variety of cluster characteristics.

[1]  K Doi,et al.  Effect of case selection on the performance of computer-aided detection schemes. , 1994, Medical physics.

[2]  M. Giger,et al.  Computer vision and artificial intelligence in mammography. , 1994, AJR. American journal of roentgenology.

[3]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.

[4]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. , 1992, Medical physics.

[5]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[6]  L. Clarke,et al.  Tree structured wavelet transform segmentation of microcalcifications in digital mammography. , 1995, Medical physics.

[7]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[8]  R Di Paola,et al.  A fractal approach to the segmentation of microcalcifications in digital mammograms. , 1995, Medical physics.

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

[10]  K L Lam,et al.  Digitization requirements in mammography: effects on computer-aided detection of microcalcifications. , 1994, Medical physics.

[11]  K Doi,et al.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. , 1990, Investigative radiology.

[12]  Y H Chang,et al.  Robustness of computerized identification of masses in digitized mammograms. A preliminary assessment. , 1996, Investigative radiology.

[13]  K L Lam,et al.  Image compression in digital mammography: effects on computerized detection of subtle microcalcifications. , 1996, Medical physics.