Mass detection in digitized mammograms using two independent computer-assisted diagnosis schemes.

OBJECTIVE Using two independent computer-assisted diagnosis (CAD) schemes, we investigated the potential to improve the sensitivity of mass detection by applying a logical "or" operation and to improve the specificity using a logical "and" operation. MATERIALS AND METHODS Two independent mass detectors, one with Gaussian bandpass filtering and multilayer topographic feature analysis and the other with a five-stage search for a single suspicious region, were applied to a large image database that included 428 digitized mammograms with 220 verified masses. The performance of the two schemes and a combination of them in the form of either logical "or" or logical "and" operations were compared. RESULTS In this preliminary study, a multilayer topographic feature analysis CAD scheme (CAD-1) achieved a sensitivity of 96% and had a false-positive detection rate of 0.79 per image. A five-stage search method scheme (CAD-2) achieved a sensitivity of 94% and had a false-positive detection rate of 1.69 per image. With an "or" operation, the combined results yielded 100% sensitivity with a false-positive detection rate of 2.07 per image. A logical "and" operation produced a reduction of the false-positive detection rate to 0.4 per image, but sensitivity also decreased to 90%. CONCLUSION Similar to an independent double-reading approach and depending upon the relevant clinical question, sensitivity or specificity can be improved by combining the results of several independent CAD schemes.

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

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

[3]  D Brzakovic,et al.  An approach to automated detection of tumors in mammograms. , 1990, IEEE transactions on medical imaging.

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

[5]  W F Bischof,et al.  Automated detection and classification of breast tumors. , 1992, Computers and biomedical research, an international journal.

[6]  C E Metz,et al.  Gains in Accuracy from Replicated Readings of Diagnostic Images , 1992, Medical decision making : an international journal of the Society for Medical Decision Making.

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

[8]  K Doi,et al.  Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses. , 1993, Investigative radiology.

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

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

[11]  What are the issues in the double reading of mammograms? , 1994, Radiology.

[12]  E. Thurfjell,et al.  Benefit of independent double reading in a population-based mammography screening program. , 1994, Radiology.

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

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

[15]  Ping Lu,et al.  Computerized detection of mammographic lesions: performance of artificial neural network with enhanced feature extraction , 1995, Medical Imaging.

[16]  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.

[17]  N. Petrick,et al.  Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. , 1995, Medical physics.

[18]  R M Nishikawa,et al.  Image feature analysis and computer-aided diagnosis in mammography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis. , 1995, Medical physics.

[19]  Ping Lu,et al.  Initial experience with a prototype clinical intelligent mammography workstation for computer-aided diagnosis , 1995, Medical Imaging.

[20]  Berkman Sahiner,et al.  Automated detection of breast masses on digital mammograms using adaptive density-weighted contrast-enhancement filtering , 1995, Medical Imaging.

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

[22]  K L Lam,et al.  Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. , 1995, Medical physics.

[23]  Y H Chang,et al.  Adaptive computer-aided diagnosis scheme of digitized mammograms. , 1996, Academic radiology.

[24]  Y H Chang,et al.  Computerized identification of suspicious regions for masses in digitized mammograms. , 1996, Investigative radiology.