Academic radiology · 2002
Computer-aided diagnosis of masses with full-field digital mammography.
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Li Lihua, Clark Robert A, Thomas Jerry A
Аннотация
RATIONALE AND OBJECTIVES: The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM).
MATERIALS AND METHODS: The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before.
RESULTS: With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing.
CONCLUSION: The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.
MATERIALS AND METHODS: The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before.
RESULTS: With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing.
CONCLUSION: The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.