Detection of clustered microcalcifications in small field digital mammography.

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.

Dynamics of water at membrane surfaces: Effect of headgroup structure.

Atomistic molecular dynamics simulations of fully hydrated 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC), 1-palmitoyl-2-oleoyl-phosphatidylethanolamine (POPE), and 1-palmitoyl-2-oleoyl-phosphatidylglycerol (POPG) bilayers in the liquid-crystalline state were carried out to investigate the effect of different lipid headgroups on the dynamics of water at the bilayer surface in short 80 ps time scales. Results obtained in these studies show that the hydrogen bonding amine group of POPE and the glycerol group of POPG slow water motion more than the equivalent choline group of POPC. Therefore, it is surprising that the effect of a POPC bilayer surface on water dynamics is similar to that of POPE and POPG bilayers. That result is due to a much higher number of water molecules interacting with the choline group of POPC than hydrogen-bonded molecules interacting with amine or glycerol groups of POPE and POPG.

Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms.

We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones.