Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

Glaucoma is a disease characterized by damaging the optic nerve head, this can result in severe vision loss. An early detection and a good treatment provided by the ophthalmologist are the keys to preventing optic nerve damage and vision loss from glaucoma. Its screening is based on the manual optic cup and disc segmentation to measure the vertical cup to disc ratio (CDR). However, obtaining the regions of interest by the expert ophthalmologist can be difficult and is often a tedious task. In most cases, the unlabeled images are more numerous than the labeled ones.We propose an automatic glaucoma screening approach named Super Pixels for Semi-Supervised Segmentation “SP3S”, which is a semi-supervised superpixel-by-superpixel classification method, consisting of three main steps. The first step has to prepare the labeled and unlabeled data, applying the superpixel method and bringing in an expert for the labeling of superpixels. In the second step, We incorporate prior knowledge of the optic cup and disc by including color and spatial information. In the final step, semi-supervised learning by the Co-forest classifier is trained only with a few number of labeled superpixels and a large number of unlabeled superpixels to generate a robust classifier. For the estimation of the optic cup and disc regions, the active geometric shape model is used to smooth the disc and cup boundary for the calculation of the CDR. The obtained results for glaucoma detection, via an automatic cup and disc segmentation, established a potential solution for glaucoma screening. The SP3S performance shows quantitatively and qualitatively similar correspondence with the expert segmentation, providing an interesting tool for semi-automatic recognition of the optic cup and disc in order to achieve a medical progress of glaucoma disease.     

Synthesis and pharmacological activity of two derivatives of the amide of valproic acid

Valproic acid (VPA), a synthetic branched-chain fatty acid, and its pro-drug the primary amide (VPD) are effective and widely used anti-epileptic agents. Although the use of VPA has grown in recent years, major side effects are still associated with this drug. We presume that it is possible, without loosing the VPD pharmacological profile, to obtain new compounds by undertaking substitutions in the CONH group. N,N'-bis-(2-propylpentanoyl)- 1,2-ethanediamine (3) and N,N'-bis-(2-propylpentanoyl)-1,3-propanediamine (4) were obtained from VPA (1) using a method reported in the literature. The chemical structures of the new compounds were demonstrated by elemental analysis, IR, and 1H NMR spectroscopy. Both compounds are less toxic and more effective in protecting the animals from death caused by PTZ than VPD after intraperitoneal administration to mice.     

Instances selection algorithm by ensemble margin

Abstract

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.     

Correction to: On Formal Modeling and Validation of Wireless Sensor Network Protocols

Wireless Personal Communications -     

An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation

Pattern Analysis and Applications - Instance and variable selection involve identifying a subset of instances and variables such that the learning process will use only this subset with better...     

An Objective Function to Evaluate Performance of Human–Robot Collaboration in Target Recognition Tasks

Robotic systems in unstructured environments must cope with unknown, unpredictable, and dynamic situations. Inherent uncertainty, and limited sensor accuracy and reliability impede target recognition performance. Introducing a human operator into the system can help improve performance and simplify the robotic system. In this paper, four basic levels of collaboration were defined for human-robot collaboration in target recognition tasks. An objective function that includes operational and time costs was developed to quantify performance and determine the best collaboration level. Signal detection theory was applied to evaluate system performance. The optimal collaboration level for different cases was determined by using numerical analyses of the objective function. The findings indicate that the best system performance, the optimal values of performance measures, and the best collaboration level depend on the task, the environment, human and robot parameters, and the system characteristics. For the tested cases, the manual level was never the best collaboration level for achieving the optimal solution. The autonomous level was the best collaboration level when robot sensitivity was higher than human sensitivity. In general, collaboration of human and robot in target recognition tasks will improve upon the optimal performance of a single human detector.     

Laser and Photonic Systems Integration: Emerging Innovations and Framework for Research and Education

The purpose of this article is to review the key emerging innovations in laser and photonics systems as well as their design and integration, focusing on challenges and opportunities for solutions of societal challenges. Developments, their significance, and frontier challenges are explained in advanced manufacturing, biomedicine and healthcare, and communication. Systems, networks, and integration issues and challenges are then discussed, and an integration framework for networking laser‐ and photonic‐based services and products is proposed. The article concludes with implications and an agenda for education, research and development, and policy needs, with a focus on human, society, science, and technology integration. © 2013 Wiley Periodicals, Inc.     

On Formal Modeling and Validation of Wireless Sensor Network Protocols

Formal verification is becoming more and more important in the field of wireless networks (WSN). The general purpose formal method called Event-B is the latest incarnation of the B Method: it is a proof based approach with a formal notation and refinement technique for modeling and verifying systems. Refinement enables implementation level features to be proven correct with respect to an abstract specification of the system. This paper proposes an initial attempt to model and verify consistency and correctness of a WSN operation in its different layers. Several formal models are introduced for this type of networks. In the first time, coloured Petri net are used to elaborate network layer models, then each one will be detailed by an Event-B formalism, while proofs are carried out using the RODIN platform which is an integrated development framework for Event-B.