Automated image analysis techniques for digital mammography

In this work we have examined the application of computer image analysis techniques to digitized mammographic images for the purpose of detecting two types of mammographic abnormalities, namely clustered microcalcifications and spiculated lesions. We have used three separate image data sets for the experiments. Two of the data sets have been used in the previous work of other researchers, thus permitting a direct comparison to their results. Our algorithms are region-oriented approaches. Thus, classification is performed on a region of interest, or object, that is segmented from the image. A set of features is computed for each object, and statistical classification is used to label the objects as either normal or abnormal. We have examined the performance of five classifiers. They are: a Linear Bayesian (LC), a Quadratic Bayesian (QC), a K-Nearest Neighbor (KNN), a Binary Decision Tree (BDT), and an Artificial Neural Network (ANN) classifier. Receiver Operating Characteristic (ROC) analysis is utilized to evaluate the performance of the classifiers. We introduce a novel method for generating ROC curves for backpropagation ANNs. We introduce a method (QESFS) for automatic feature selection for determining a nearly optimal feature vector from a pool of many features. The conclusions that can be drawn from this work are: (1) The method introduced here for deriving an ROC for a backpropagation ANN classifier provides superior performance to the currently used methods. (2) The QESFS approach to feature selection introduced here provides a computationally practical way of choosing what should be a nearly optimal feature vector. (3) The apparently most useful features for the problems addressed in this work are texture, shape, and contrast. (4) Feature vectors of moderate size (6 to 8 features) are appropriate for the problems addressed here. (5) The KNN classifier provides generally superior performance for the problems addressed here (relative to the other classifiers: LC, QC, BDT, ANN). (6) Spiculated detection appears to require at least 280 microns per pixel of spatial resolution. (7) Microcalcification detection does appear to require at least 50 microns per pixel of spatial resolution. (8) A KNN classifier appears to make use of information contained in the 10th bit or greater of intensity resolution for spiculated lesion detection. A QC classifier for the same problem appears to need only 8 bits to achieve only slightly lower performance. (9) Both QC and KNN appear to require 10 bits or more of intensity resolution for microcalcification detection. (10) The region-oriented approaches explored here appear to lag behind the pixel-oriented approaches explored by other researchers. Possible explanations for this involve the different volumes of training data possible in the two approaches when the total number of images is still "small", the ability of the pixel-oriented approach to defer declaring an object until after individual pixels of the object have been examined, and the need of the region-oriented approaches to be very liberal in the initial segmentation stage in order to maintain high sensitivity. (Abstract shortened by UMI.)