There are approximately 6000 hospitals in the United States, of which approximately 5400 employ minimally
invasive surgical robots for a variety of procedures. Furthermore, 95% of these robots require extensive
registration before they can be fitted into the operating room. These "registrations" are performed by surgical
navigation systems, which allow the surgical tools, the robot and the surgeon to be synchronized together-hence
operating in concert. The most common surgical navigation modalities include: electromagnetic (EM) tracking
and optical tracking. Currently, these navigation systems are large, intrusive, come with a steep learning curve,
require sacrifices on the part of the attending medical staff, and are quite expensive (since they require several
components). Recently, photoacoustic (PA) imaging has become a practical and promising new medical imaging
technology. PA imaging only requires the minimal equipment standard with most modern ultrasound (US) imaging
systems as well as a common laser source. In this paper, we demonstrate that given a PA imaging system, as
well as a stereocamera (SC), the registration between the US image of a particular anatomy and the SC image
of the same anatomy can be obtained with reliable accuracy. In our experiments, we collected data for N = 80
trials of sample 3D US and SC coordinates. We then computed the registration between the SC and the US
coordinates. Upon validation, the mean error and standard deviation between the predicted sample coordinates
and the corresponding ground truth coordinates were found to be 3.33 mm and 2.20 mm respectively.
There is considerable research in the field of content-based image retrieval (CBIR); however, few of the current
systems incorporate radiologists' visual impression of image similarity. Our objective is to bridge the semantic
gap between radiologists' ratings and image features. We have been developing a conceptual-based similarity
model derived from content-based similarity to improve CBIR. Previous work in our lab reduced the Lung Image
Database Consortium (LIDC) data set into a selection of 149 images of unique nodules, each containing nine
semantic ratings by four radiologists and 64 computed image features. After evaluating the similarity measures
for both content-based and semantic-based features, we selected 116 nodule pairs with a high correlation between
both similarities. These pairs were used to generate a linear regression model that predicts semantic similarity
with content similarity input with an R2 value of 0.871. The characteristics and features of nodules that were
used for the model were also investigated.
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