Automatic flight path generation in a virtual colonoscopy system

Virtual colonoscopy is a computerized procedure to examine colonic polyps from a CT data set. To automatically fly through a long and complex-shaped colon with a virtual camera, we propose an efficient method to simultaneously generate view-positions and view-directions. After obtaining a 3-D binary colon model, we find an initial path that represents rough camera directions and positions along it. Then, by using this initial path, we generate control planes to find a set of discrete view-positions, and view planes to obtain the corresponding view-directions, respectively. Finally, for continuous and smooth navigation, the obtained view-positions and directions are interpolated using the B-spline method. Here, by imposing a constraint to control planes, penetration and collision can be avoided in the interpolated result. Effectiveness of the proposed algorithm is examined via computer simulations using the several phantoms to simulate the characteristics of human colon, namely, high-curvatures and complex structure. Simulation results show that the algorithm provides the view-positions and view-directions suitable for covering more 3-D surface area in the navigation. Also, prospective results are obtained for human colon data with a high processing speed of less than 1 minutewitha2GHzstandardPC

Lectin histochemistry of Kudoa septempunctata genotype ST3-infected muscle of olive flounder (Paralichthys olivaceus)

The localization of carbohydrate terminals in Kudoa septempunctata ST3-infected muscle of olive flounder (Paralichthys olivaceus) was investigated using lectin histochemistry to determine the types of carbohydrate sugar residues expressed in Kudoa spores. Twenty-one lectins were examined, i.e., N-acetylglucosamine (s-WGA, WGA, DSL-II, DSL, LEL, STL), mannose (Con A, LCA, PSA), galactose/N-acetylgalactosamine (RCA12, BSL-I, VVA, DBA, SBA, SJA, Jacalin, PNA, ECL), complex type N-glycans (PHA-E and PHA-L), and fucose (UEA-I). Spores encased by a plasmodial membrane were labeled for the majority of these lectins, with the exception of LCA, PSA, PNA, and PHA-L. Four lectins (RCA 120, BSL-I, DBA, and SJA) belonging to the galactose/N-acetylgalactosamine group, only labeled spores, but not the plasmodial membrane. This is the first confirmation that various sugar residues are present in spores and plasmodial membranes of K. septempunctata ST3

Lectin histochemistry of

The localization of carbohydrate terminals in Kudoa septempunctata ST3-infected muscle of olive flounder (Paralichthys olivaceus) was investigated using lectin histochemistry to determine the types of carbohydrate sugar residues expressed in Kudoa spores. Twenty-one lectins were examined, i.e., N-acetylglucosamine (s-WGA, WGA, DSL-II, DSL, LEL, STL), mannose (Con A, LCA, PSA), galactose/N-acetylgalactosamine (RCA12, BSL-I, VVA, DBA, SBA, SJA, Jacalin, PNA, ECL), complex type N-glycans (PHA-E and PHA-L), and fucose (UEA-I). Spores encased by a plasmodial membrane were labeled for the majority of these lectins, with the exception of LCA, PSA, PNA, and PHA-L. Four lectins (RCA 120, BSL-I, DBA, and SJA) belonging to the galactose/N-acetylgalactosamine group, only labeled spores, but not the plasmodial membrane. This is the first confirmation that various sugar residues are present in spores and plasmodial membranes of K. septempunctata ST3

Low-Power Computer Vision: Status, Challenges, Opportunities

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries and energy efficiency is critical. This article serves two main purposes: (1) Examine the state-of-the-art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This article summarizes 2018 winners\u27 solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision