One challenge facing radiologists is the characterization of whether a pulmonary nodule detected in a CT scan is likely to be benign or malignant. We have developed an image processing and machine learning based computer-aided diagnosis (CADx) method to support such decisions by estimating the likelihood of malignancy of pulmonary nodules. The system computes 192 image features which are combined with patient age to comprise the feature pool. We constructed an ensemble of 1000 linear discriminant classifiers using 1000 feature subsets selected from the feature pool using a random subspace method. The classifiers were trained on a dataset of 125 pulmonary nodules. The individual classifier results were combined using a majority voting method to form an ensemble estimate of the likelihood of malignancy. Validation was performed on nodules in the Lung Imaging Database Consortium (LIDC) dataset for which radiologist interpretations were available. We performed calibration to reduce the differences in the internal operating points and spacing between radiologist rating and the CADx algorithm. Comparing radiologists with the CADx in assigning nodules into four malignancy categories, fair agreement was observed (κ=0.381) while binary rating yielded an agreement of (κ=0.475), suggesting that CADx can be a promising second reader in a clinical setting.
Computer-aided detection (CAD) algorithms 'automatically' identify lung nodules on thoracic multi-slice CT scans
(MSCT) thereby providing physicians with a computer-generated 'second opinion'. While CAD systems can achieve
high sensitivity, their limited specificity has hindered clinical acceptance. To overcome this problem, we propose a false
positive reduction (FPR) system based on image processing and machine learning to reduce the number of false positive
lung nodules identified by CAD algorithms and thereby improve system specificity.
To discriminate between true and false nodules, twenty-three 3D features were calculated from each candidate nodule's
volume of interest (VOI). A genetic algorithm (GA) and support vector machine (SVM) were then used to select an
optimal subset of features from this pool of candidate features. Using this feature subset, we trained an SVM classifier to
eliminate as many false positives as possible while retaining all the true nodules. To overcome the imbalanced nature of
typical datasets (significantly more false positives than true positives), an intelligent data selection algorithm was
designed and integrated into the machine learning framework, thus further improving the FPR rate.
Three independent datasets were used to train and validate the system. Using two datasets for training and the third for
validation, we achieved a 59.4% FPR rate while removing one true nodule on the validation datasets. In a second
experiment, 75% of the cases were randomly selected from each of the three datasets and the remaining cases were used
for validation. A similar FPR rate and true positive retention rate was achieved. Additional experiments showed that the
GA feature selection process integrated with the proposed data selection algorithm outperforms the one without it by
5%-10% FPR rate.
The methods proposed can be also applied to other application areas, such as computer-aided diagnosis of lung nodules.
We have grown p-type ZnSe1-xTex:N (x equals 0.08 - 1.0) epilayers by molecular beam epitaxy on GaAs substrates, and characterized their electrical behavior. The Te fraction x was determined by energy-dispersive x-ray spectroscopy and by high-resolution x-ray diffraction. The free-hole concentrations and mobilities were determined by Hall-effect measurements, and the contact resistances of evaporated PdAu metal to the epilayers were measured using standard transmission-line techniques. The contact resistance decreases sharply with increasing Te content, falling from 0.6 (Omega) cm2 for a film with 8% Te to 3.5 multiplied by 10-7 (Omega) cm2 for a pure ZnTe film. Under the growth and doping conditions used, the hole mobility shows a minimum of about 1 cm2/Vs at about 25% Te. It is expected that by optimizing these single-layer properties, the building blocks of an improved electrical contact to ZnSe can be obtained.
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