Study On Microcalcifications Detecting Technique In Mammograms

Though lots of investigations on the microcalcification detecting technique were done by many experts home and abroad in the past 20 years, there still exists the major problem that the targets detecting results expressed a low sensitivity with a high false-positive rate. We'v done research on this problem and investigated to extract microcalcification's features, to solve this problem with the knowledge of Mathematical Morphology and Machine Learning Classifier.The microcalcification detecting technique investigated by our dissertation includes two parts as follows: Firstly, depending on the microcalcifications'morphology information that they're tiny granule and approximate round in shape, we contrive a multi-structure-access morph-grads enhancement algorithm based on Mathematical Morphology, and regions with the similar morph-features to the microcalcifications will get a higher brightness, then we detect the mircocalcification targets with the iterative ordinal filter, and the experiment results prove that the target's morph-enhancement algorithm can heighten the detecting sensitivity, but the there are still a number of false positives in our results.In order to reduce the amount of false positives in our coarse detecting results, we do analysis and validations to extract those efficient features both in spatial and wavelet domain from moiety of our coarse detecting results which are chosen randomly, as the training samples to SVM classifier, those unchosen detecting results can be the test samples of the trained SVM classifier, and a large quantity of false positives in those test samples will be wiped off from the classifier's adjudication. The experiment results prove that lots of false positives are wiped off by SVM classifier, so false-positive rate can be remarkable decreased, and the targets detectability is improved.Experiments express that the approach investigated by our dissertation has a good performance on the microcalcification targets detection, we can even detect targets correctly in very dense mammograms, also with low false-positive rate. Our experiment expresses that we have 87.1% as sensitive rate and 12.2% false-positive rate. Thus, we solve those so far existing major problems in the microcalcification targets detecting field.