0000000000199137
AUTHOR
Filippo Millonzi
A memetic approach to discrete tomography from noisy projections
Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of no…
An Island Strategy for Memetic Discrete Tomography Reconstruction
In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in …
Discrete Tomography Reconstruction Through a New Memetic Algorithm
Discrete tomography is a particular case of computerized tomography that deals with the reconstruction of objects made of just one homogeneous material, where it is sometimes possible to reduce the number of projections to no more than four. Most methods for standard computerized tomography cannot be applied in the former case and ad hoc techniques must be developed to handle so few projections.
A Memetic Island Model for Discrete Tomography Reconstruction
Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawba…
A Memetic Algorithm for Binary Image Reconstruction
This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.
Modelling the Effects of Internal Textures on Symmetry Detection Using Fuzzy Operators
Symmetry is a crucial dimension which aids the visual system, human as well as artificial, to organize its environment and to recognize forms and objects. In humans, detection of symmetry, especially bilateral and rotational, is considered to be a primary factor for discovering and interacting with the surrounding environment. Rotational symmetry detecting can be affected by less-known factors, such as the stimulus internal texture. This paper explores how fuzzy operators can be usefully employed in modeling the effects of the internal texture on symmetry detection. To this aim, we selected two symmetry detection algorithms, based on different computational models, and compared their output…
A cloud based architecture to support Electronic Health Record.
We introduce a novel framework of electronic healthcare enabled by a Cloud platform able to host both Hospital Information Systems (HIS) and Electronic Medical Record (EMR) systems and implement an innovative model of Electronic Health Record (EHR) that is not only patient-oriented but also supports a better governance of the whole healthcare system. The proposed EHR model adopts the state of the art of the Cloud technologies, being able to join the different clinical data of the patient stored within the HISs and EMRs either placed into a local Data Center or hosted into a Cloud Platform enabling new directions of data analysis.