A learning approach to detecting lung nodules in CT Images.

Abstract

Lung cancer is one of the most common types of cancer and has the highest mortality rate. Unfortunately, it is a long and difficult process for the physician to detect the presence of this disease. He/she must search through three-dimensional medical images and look for possibly cancerous, small structures that are roughly spherical. These structures are called pulmonary nodules. Due to the difficult and time consuming detection task faced by the physician, computer-aided detection (CAD) has been the focus of many research efforts. Most of these works involve segmenting the image into structures, extracting features from the structures, and classifying the resulting feature vectors. Unfortunately, the first of these tasks, segmentation, is a difficult problem and many times the origin for missed detections. This work attempts to eliminate the structure segmentation step. Instead, features are extracted from fixed size subwindows and sent to a classifier. Bypassing the segmentation step allows for every location to be classified. Feature extraction is accomplished by learning a complete basis for the subwindow on the training set and using the inner product of the subwindow with each basis element. This approach is preferred over choosing features based on human interpretation, as the latter approach will most likely result in valuable information being overlooked. The bases used are derived from the singular value decomposition (SVD), a modification of the SVD, tensor decompositions, vectors reminiscent of the Haar wavelets, and the Fourier basis. The features are sent to a number of different classifiers for comparison. The classifiers include parametric methods such as likelihood classifiers and probabilistic clustering, as well as non-parametric classifiers such as kernel support vector machines (SVM), classification trees, and AdaBoost. While different feature and classifiers bring about a wide range of results, the non-parametric classifiers unsurprisingly produce much better detection and false positive rates. The best combination on the test set yields 100\% detection of the nodule subwindows, while only classifying 1\% of the non-nodule windows as nodules. This is in comparison to previous CAD approaches discussed in this thesis which achieve no better than 85\% detection rates.