In this paper we present a prototype for an automated deception detection system. Similar to polygraph examinations, we
attempt to take advantage of the theory that false answers will produce distinctive measurements in certain physiological
manifestations. We investigate the role of dynamic eye-based features such as eye closure/blinking and lateral movements
of the iris in detecting deceit. The features are recorded both when the test subjects are having non-threatening conversations
as well as when they are being interrogated about a crime they might have committed. The rates of the behavioral
changes are blindly clustered into two groups. Examining the clusters and their characteristics, we observe that the dynamic
features selected for deception detection show promising results with an overall deceptive/non-deceptive prediction
rate of 71.43% from a study consisting of 28 subjects.
Esophageal ultrasound (EUS) is particularly useful for isolating lymph nodes in the N-staging of esophageal
cancer, a disease with very poor overall prognosis. Although EUS is relatively low-cost and real time, and it
provides valuable information to the clinician, its usefulness to less trained "users" including opportunities for
computer-aided diagnosis is still limited due to the strong presence of spatially correlated interference noise
called speckles. To this end, in this paper, we present a technique for enhancing lymph nodes in EUS images
by first reducing the spatial correlation of the specular noise and then using a modified structured tensor-based
anisotropic filter to complete the speckle reduction process. We report on a measure of the enhancement and
also on the extent of automatic processing possible, after the speckle reduction process has taken place. Also, we
show the limitations of the enhancement process by extracting relevant lymph node features from the despeckled
images. When tested on five representative classes of esophageal lymph nodes, we found the despeckling process
to greatly reduce the specularity of the original EUS images, therefore proving very useful for visualization
purposes. But it still requires additional work for the complete automation of the lymph node characterizing
process.
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