Computer-aided diagnostic characterization (CADc) aims to support medical imaging decision making by objectively
rating the radiologists' subjective, perceptual opinions of visual diagnostic characteristics of suspicious lesions. This
research uses the publicly available Lung Image Database Consortium (LIDC) collection of radiologists' outlines of
nodules and ratings of boundary and shape characteristics: spiculation, margin, lobulation, and sphericity. The approach
attempts to reduce the observed disagreement between radiologists on the extent of nodules by combining their spatial
opinion using probability maps to create regions of interest (ROIs). From these ROIs, images features are extracted and
combined using machine learning models to predict a combined opinion, the median rating and a thresholded, binary
version of their diagnostic characteristics. The results show slight to fair agreement-linear-weighted Kappa-between
the CADc models and median radiologist opinion for the full scale five-level rating and fair to moderate agreement using
a binary version of the median radiologist opinion.
Using computer-calculated features to characterize the shape of suspicious lesions aims to assist the diagnosis of
pulmonary nodules; moreover, these computerized features have to be in agreement with radiologists' ratings
measuring their human perception of the nodules' shape. In the Lung Image Database Consortium (LIDC), there
exists strong disagreement among the radiologists on the ratings of the shape diagnostic characteristics as well as on
their drawn outlines of the extent of the nodules. Since shape is often considered a property of the object boundary
and the manual boundaries are not consistent among radiologists, new methods are necessary to, first, define regionbased
boundaries that use radiologists' outlines as guides and, second, adapt computer-based shape measurements
to use regions rather than the traditional nodule segmentation outlines. This paper introduces a method for defining a
boundary region of interest by combining radiologist-drawn outlines (the pixel-set difference between the union and
intersection of all radiologist-drawn outlines for a specific nodule), then adapts a radial gradient indexing method for
use within image regions, and lastly predicts several composite ratings of sets of radiologists for shape-based
characteristics: spiculation, lobulation, and sphericity. The prediction of the majority (mode) rating significantly
outperforms earlier work on predicting the ratings of individual radiologists. The prediction of spiculation improves
to 53% from 41%, lobulation increases to 44% from 38%, and sphericity improves to 58% from 43%. A binary
version of the rating has high accuracy but poor Kappa agreement for all three shape characteristics.
Texture analysis and classification of soft tissues in Computed Tomography (CT) images
recently advanced with a new approach that disambiguates the checkboard problem
where two distinctly different patterns produce identical co-occurrence matrices, but this
method quadruples the size of the feature space. The feature space size problem is
exacerbated by the use of varying sized texture operators for improving boundary
segmentation. Dimensionality reduction motivates this investigation into systematic
analysis of the power of feature categories (Haralick descriptors, distance, and direction)
to differentiate between soft tissues.
The within-organ variance explained by the individual components of feature categories
offers a ranking of their potential power for between-organ discrimination. This paper
introduces a technique for combining the Principal Component Analysis (PCA) results to
compare and visualize the explanatory power of features with varying window sizes. We
found that 1) the two Haralick features Cluster Tendency and Contrast contribute the
most; 2) as distance increases, its contribution to overall variance decreases; and 3)
direction is unimportant.
We also evaluated the proposed technique with respect to its classification power. Linear
Discriminant Analysis (LDA) and Decision Tree (DT) were used to produce two
classification models based on the reduced data set. We found that using PCA either fails
to improve or markedly degrades the classification performance of LDA as well as of the
DT model. Though feature extraction for classification shows no promise, the proposed
technique offers a systematic mechanism to compare feature reduction strategies for
varying window sizes as well as other measurement techniques.
Visualization techniques for simulations are often limited to statistical reports, graphs, and charts, but simulations can be enhanced through the use of animation. A spatio-temporal animation allows a viewer to observe a simulation operate, rather than deduce it from numerical output. The Route Viewer, developed by Argonne National Laboratory, is a two-dimensional animation model that animates the objects and events produced by a discrete event simulation. It operates in a playback mode, whereby a simulated scenario is animated after the simulation has completed. The Route Viewer is used to verify the simulation's processes and data, but it also benefits the simulation as an analytical tool by facilitating spatial and temporal analysis. By visualizing the events of a simulated scenario in two-dimensional space, it is possible to determine whether the scenario, or simulation model, is reasonable. Further, the Route Viewer provides an awareness of what happens in a scenario, when it happens, and the completeness and efficiency of the scenario and its processes. For Army deployments, it highlights utilization of resources and where bottlenecks are occurring. This paper discusses how the Route Viewer facilitates the analysis of military deployment simulation model results.