Computer-aided detection of clustered microcalcifications in digitized mammograms.

RATIONALE AND OBJECTIVES We investigated a computer-aided detection (CAD) scheme for clustered microcalcifications in digitized mammograms. METHODS A multistage CAD scheme was developed and tested. To increase sensitivity, the scheme uses a Gaussian band-pass filter and nonlinear threshold. A multistage local minimum searching routine and a multilayer topographic feature analysis are used to reduce the false-positive detection rate. One hundred ten digitized mammograms were used in this preliminary test, with 55 images containing one or two verified microcalcification clusters. RESULTS The CAD scheme achieved 100% sensitivity and had an average false-positive detection rate of 0.18 per image. CONCLUSION The CAD scheme performs as well as many published schemes and has some unique advantages to further improve detection sensitivity and specificity of future CAD schemes.

[1]  K Doi,et al.  Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals. , 1993, Medical physics.

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

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

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

[5]  Jacob Beutel,et al.  Application of neural networks to computer-aided pathology detection in mammography , 1993, Medical Imaging.

[6]  K. Doi,et al.  Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study. , 1988, Investigative radiology.

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

[8]  N Karssemeijer,et al.  Spatial Resolution in Digital Mammography , 1993, Investigative radiology.

[9]  D R Dance,et al.  The automatic computer detection of subtle calcifications in radiographically dense breasts. , 1992, Physics in medicine and biology.

[10]  Dong-Ming Zhao.  Rule-based morphological feature extraction of microcalcifications in mammograms , 1993, Electronic Imaging.

[11]  Carey E. Priebe,et al.  Comparative evaluation of pattern recognition techniques for detection of microcalcifications , 1993, Electronic Imaging.

[12]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. , 1994, Medical physics.

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

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

[15]  P. F. Winter,et al.  Algorithm for the detection of fine clustered calcifications on film mammograms. , 1988, Radiology.

[16]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[17]  A. I. Cohn,et al.  Expert system-controlled image display. , 1989, Radiology.

[18]  D. Dance,et al.  Automatic computer detection of clustered calcifications in digital mammograms , 1990, Physics in medicine and biology.

[19]  L. Kuncheva,et al.  Two-level classification schemes in medical diagnostics. , 1993, International journal of bio-medical computing.

[20]  Ping Lu,et al.  Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms , 1993, Electronic Imaging.