2,601 research outputs found
Motion correction of PET/CT images
Indiana University-Purdue University Indianapolis (IUPUI)The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses.
In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study
Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
We demonstrate that particle swarm optimisation (PSO) can be used to solve a variety of problems arising during operation of a digital inspection microscope. This is a use case for the feasibility of heuristics in a real-world product. We show solutions to four measurement problems, all based on PSO. This allows for a compact software implementation solving different problems. We have found that PSO can solve a variety of problems with small software footprints and good results in a real-world embedded system. Notably, in the microscope application, this eliminates the need to return the device to the factory for calibration
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20
Object Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially occluded Targets
Motion estimation, object detection, and tracking have been actively pursued by researchers in the field of real time video processing. In the present work, a new algorithm is proposed to automatically detect objects using revised local binary pattern (m-LBP) for object detection. The detected object was tracked and its location estimated using the Kalman filter, whose state covariance matrix was tuned using particle swarm optimisation (PSO). PSO, being a nature inspired algorithm, is a well proven optimization technique. This algorithm was applied to important real-world problems of partially-occluded objects in infrared videos. Algorithm validation was performed by realizing a thermal imager, and this novel algorithm was implemented in it to demonstrate that the proposed algorithm is more efficient and produces better results in motion estimation for partially-occluded objects. It is also shown that track convergence is 56% faster in the PSO-Kalman algorithm than tracking with Kalman-only filter
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network
The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations
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