Nuclear feature extraction for breast tumor diagnosis

Abstract

Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86%.