| 期刊全称 | Bioinspired Applications in Artificial and Natural Computation | | 期刊简称 | Third International | | 影响因子2023 | José Mira,José Manuel Ferrández,F. Javier Toledo | | 视频video | http://file.papertrans.cn/188/187249/187249.mp4 | | 学科分类 | Lecture Notes in Computer Science | | 图书封面 |  | | 影响因子 | The two-volume set LNCS 5601 and LNCS 5602 constitutes the refereed proceedings of the Third International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2009, held in Santiago de Compostela, Spain, in June 2009. The 108 revised papers presented are thematically divided into two volumes. The first volume includes papers relating the most recent collaborations with Professor Mira and contributions mainly related with theoretical, conceptual and methodological aspects linking AI and knowledge engineering with neurophysiology, clinics and cognition. The second volume contains all the contributions connected with biologically inspired methods and techniques for solving AI and knowledge engineering problems in different application domains. | | Pindex | Conference proceedings 2009 |
| 1 |
Front Matter |
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Abstract
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| 2 |
,Measurements over the Aquiles Tendon through Ecographic Images Processing, |
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Abstract
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| 3 |
,A New Approach in Metal Artifact Reduction for CT 3D Reconstruction, |
Valery Naranjo,Roberto Llorens,Patricia Paniagua,Mariano Alcañiz,Salvador Albalat |
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Abstract
The 3D representation of CT scans is widely used in medical application such as virtual endoscopy, plastic reconstructive surgery, dental implant planning systems and more. Metallic objects present in CT studies cause strong artifacts like beam hardening and streaking, what difficult to a large extent the 3D reconstruction. Previous works in this field use projection data in different ways with the aim of artifact reduction. But in DICOM-based applications this information is not available, thus the need for a new point of view regarding this issue. Our aim is to present an exhaustive study of the state of the art and to evaluate a new approach based in mathematical morphology in polar domain in order to reduce the noise but preserving dental structures, valid for real-time applications.
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| 4 |
,Genetic Approaches for the Automatic Division of Topological Active Volumes, |
J. Novo,N. Barreira,M. G. Penedo,J. Santos |
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Abstract
The Topological Active Volumes is an active model focused on 3D segmentation tasks. It is based on the 2D Topological Active Nets model and provides information about the surfaces and the inside of the detected objects in the scene. This paper proposes new optimization approaches based on Genetic Algorithms combined with a greedy local search that improve the results of the 3D segmentations and overcome some drawbacks of the model related to parameter tuning or noise conditions. The hybridization of the genetic algorithm with the local search allows the treatment of topological changes in the model, with the possibility of an automatic subdivision of the Topological Active Volume. This combination integrates the advantages of the global and local search procedures in the segmentation process.
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| 5 |
,Object Discrimination by Infrared Image Processing, |
Ignacio Bosch,Soledad Gomez,Raquel Molina,Ramón Miralles |
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Abstract
Signal processing applied to pixel by pixel infrared image processing has been frequently used as a tool for fire detection in different scenarios. However, when processing the images pixel by pixel, the geometrical or spatial characteristics of the objects under test are not considered, thus increasing the probability of false alarms. In this paper we use classical techniques of image processing in the characterization of objects in infrared images. While applying image processing to thermal images it is possible to detect groups of hotspots representing possible objects of interest and extract the most suitable features to distinguish between them. Several parameters to characterize objects geometrically, such as fires, cars or people, have been considered and it has been shown their utility to reduce the probability of false alarms of the pixel by pixel signal processing techniques.
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| 6 |
,Validation of Fuzzy Connectedness Segmentation for Jaw Tissues, |
Roberto Lloréns,Valery Naranjo,Miriam Clemente,Mariano Alcañiz,Salvador Albalat |
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Abstract
Most of the dental implant planning systems implement 3D reconstructions of the CT-data in order to achieve more intuitive interfaces. This way, the dentists or surgeons can handle the patient’s virtual jaw in the space and plan the location, orientation and some other features of the implant from the orography and density of the jaw. The segmentation of the jaw tissues (the cortical bone, the trabecular core and the mandibular channel) is critical for this process, because each one has different properties and in addition, because an injury of the channel in the surgery may cause lip numbness. Current programs don’t carry out the segmentation process or just do it by hard thresholding or by means of exhaustive human interaction. This paper deals with the validation of fuzzy connectedness theory for the automated, accurate and time efficient segmentation of jaw tissues.
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,Breast Cancer Classification Applying Artificial Metaplasticity, |
Alexis Marcano-Cedeño,Fulgencio S. Buendía-Buendía,Diego Andina |
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Abstract
In this paper we are apply Artificial Metaplasticity MLP (MMLPs) to Breast Cancer Classification. Artificial Metaplasticity is a novel ANN training algorithm that gives more relevance to less frequent training patterns and subtract relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the Multilayer Perceptron performance. Wisconsin Breast Cancer Database (WBCD) was used to train and test MMLPs. WBCD is a well-used database in machine learning, neural networks and signal processing. Experimental results show that MMLPs reach better accuracy than any other recent results.
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| 8 |
,Ontology Based Approach to the Detection of Domestics Problems for Independent Senior People, |
Juan A. Botia Blaya,Jose Palma,Ana Villa,David Perez,Emilio Iborra |
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Abstract
In the first decade of the 21st century, there is a tremendous increment in the number of elderly people which live independently in their own houses. In this work, we focus on elderly people which spend almost all the time by their own. The goal of this work is to build an artificial system capable of unobtrusively monitor this concrete subject. In this case, the system must be capable of detecting potential situations of danger (e.g. the person lays unmobilised in the floor or she is suffering some kind of health crysis). This is done without any wearable device but only using a sensor network and an intelligent processing unit within a single and small CPU. This kind of such unbostrusive system makes seniors to augment his or her perception of independence and safeness at home.
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,A Wireless Sensor Network for Assisted Living at Home of Elderly People, |
Francisco Fernández-Luque,Juan Zapata,Ramón Ruiz,Emilio Iborra |
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Abstract
This paper introduces an ubiquitous wireless network infrastructure to support an assisted living at home system. This system integrates a set of smart sensors which are designed to provide care assistence and security to elderly citizens living at home alone. The system facilitates privacy by performing local computation, it supports heterogeneous sensor devices and it provides a platform and initial architecture for exploring the use of sensors with elderly people. We have developed a low-power multihop network protocol consists of nodes (Motes) that wirelessly communicate to each other and are capable of hopping radio messages to a base station where they are passed to a PC (or other possible client). The goal of this project is to provide alerts to caregivers in the event of an accident, acute illness or strange (possibly dangerous) activities, and enable monitoring by authorized and authenticated caregivers. In this paper, we describe ubiquitous assistential monitoring system at home. We have focused on the unobtrusive habitual activities signal measurement and wireless data transfer using ZigBee technology.
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| 10 |
,An Ambient Assisted Living System for Telemedicine with Detection of Symptoms, |
A. J. Jara,M. A. Zamora-Izquierdo,A. F. Gomez-Skarmeta |
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Abstract
Elderly people have a high risk of health problems. Hence, we propose an architecture for Ambient Assisted Living (AAL) that supports pre-hospital health emergencies, remote monitoring of patients with chronic conditions and medical collaboration through sharing of health-related information resources (using the European electronic health records CEN/ISO EN13606). Furthermore, it is going to use medical data from vital signs for, on the one hand, the detection of symptoms using a simple rule system (e.g. fever), and on the other hand, the prediction of illness using chronobiology algorithms (e.g. prediction of myocardial infarction eight days before). So this architecture provides a great variety of communication interfaces to get vital signs of patients from a heterogeneous set of sources, as well as it supports the more important technologies for Home Automation. Therefore, we can combine security, comfort and ambient intelligence with a telemedicine solution, thereby, improving the quality of life in elderly people.
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| 11 |
,Applying Context-Aware Computing in Dependent Environments, |
Juan A. Fraile,Javier Bajo,Juan M. Corchado |
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Abstract
Context-aware systems gather data from their surrounding environments in order to offer completely new opportunities in the development of end user applications. Used in conjunction with mobile devices, these systems are of great value and increase usability. Applications and services should adapt to changing conditions within dynamic environments. This article analyzes the important aspects of context-aware computing and shows how it can be applied to monitor dependent individuals in their home. The proposed system logically processes the data it receives in order to identify and maintain a permanent location on the patient in the home, managing the infrastructure of services both safely and securely.
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,A Smart Solution for Elders in Ambient Assisted Living, |
Nayat Sánchez-Pi,José Manuel Molina |
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Abstract
Ambient Assisted Living (AAL) includes assistance to carry out daily activities, health and activity monitoring, enhancing safety and security, getting access to, medical and emergency systems. Ambient home care systems (AHCS) are specially design for this purpose; they aim at minimizing the potential risks that living alone may suppose for an elder, thanks to their capability of gathering data of the user, inferring information about his activity and state, and taking decisions on it. In this paper, we present several categories of context-aware services. One related to the autonomy enhancement including services like: medication, shopping and cooking. And another which is the emergency assistant category designed for the assistance, prediction and prevention of any emergency occurred addressed to any elder and their caregivers. These services run on the top of an AHCS, which collects data from a network of environmental, health and physical sensors and then there is a context engine, customized on Appear platform that holds the inference and reasoning functionalities.
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,Convergence of Emergent Technologies for the Digital Home, |
Celia Gutiérrez,Sara Pérez |
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Abstract
The Digital Home is the result of the convergence of technologies of different nature that interact with each other in the Home environment. It is a realization of the Ambient Intelligence concept. The final objective of the Ambient Intelligence is that sensors, devices and networks that compose this environment can co-exist with human users, to improve their quality of live. The relevant characteristic of the Digital Home as a main scenario of Ambient Intelligence is its pervasive nature. This paper describes these technologies and their harmonization, based on the work done in the INREDIS project, which deals with accessibility and new technologies.
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| 14 |
,Results of an Adaboost Approach on Alzheimer’s Disease Detection on MRI, |
Alexandre Savio,Maite García-Sebastián,Manuel Graña,Jorge Villanúa |
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Abstract
In this paper we explore the use of the Voxel-based Morphometry (VBM) detection clusters to guide the feature extraction processes for the detection of Alzheimer’s disease on brain Magnetic Resonance Imaging (MRI). The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed over the data from the original MRI volumes and from the GM segmentation volumes, using the VBM clusters as voxel selection masks. We use the Support Vector Machine (SVM) algorithm to perform classification of patients with mild Alzheimer’s disease vs. control subjects. We have also considered combinations of isolated cluster based classifiers and an Adaboost strategy applied to the SVM built on the feature vectors. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies. Results are moderately encouraging, as we can obtain up to 85% accuracy with the Adaboost strategy in a 10-fold cross-validation.
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| 15 |
,Analysis of Brain SPECT Images for the Diagnosis of Alzheimer Disease Using First and Second Order |
D. Salas-Gonzalez,J. M. Górriz,J. Ramírez,M. López,I. Álvarez,F. Segovia,C. G. Puntonet |
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Abstract
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the selection of the voxels which present greater overall difference between both modalities (normal and Alzheimer) and also lower dispersion. We measure the dispersion of the intensity values for normals and Alzheimer images by mean of the standard deviation images. The mean value of the intensities of selected voxels is used as feature for different classifiers, including support vector machines with linear kernels, fitting a multivariate normal density to each group and the k-nearest neighbors algorithm. The proposed methodology reaches an accuracy of 92 % in the classification task.
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,Neurobiological Significance of Automatic Segmentation: Application to the Early Diagnosis of Alzhe |
Ricardo Insausti,Mariano Rincón,César González-Moreno,Emilio Artacho-Pérula,Amparo Díez-Peña,Tomás G |
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Abstract
Alzheimer’s disease is a progressive neurodegenerative disease that affects particularly memory function. Specifically, the neural system responsible for encoding and retrieval of the memory for facts and events (declarative memory) is dependent on anatomical structures located in the medial part of the temporal lobe (MTL). Clinical lesions as well as experimental evidence point that the hippocampal formation (hippocampus plus entorhinal cortex) and the adjacent cortex, both main components of the MTL, are the regions critical for normal declarative memory function. Neuroimage studies as ours, have taken advantage of the feasibility of manual segmentation of the gray matter volume, which correlates with memory impairment and clinical deterioration of Alzheimer’s disease patients. We wanted to explore the advantages of automatic segmentation tools, and present results based on one 3T MRI in a young subject. The automatic segmentation allowed a better discrimination between extracerebral structures and the surface of the brain, as well as an improvement both in terms of speed and reliability in the demarcation of different MTL structures, all of which play a key role in declarative m
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| 17 |
,Support Vector Machines and Neural Networks for the Alzheimer’s Disease Diagnosis Using PCA, |
M. López,J. Ramírez,J. M. Górriz,I. Álvarez,D. Salas-Gonzalez,F. Segovia,M. Gómez-Río |
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Abstract
In the Alzheimer’s Disease (AD) diagnosis process, functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians. However, the current evaluation of these images entails a succession of manual reorientations and visual interpretation steps, which attach in some way subjectivity to the diagnostic. In this work, two pattern recognition methods have been applied to SPECT and PET images in order to obtain an objective classifier which is able to determine whether the patient suffers from AD or not. A common feature selection stage is first described, where Principal Component Analysis (PCA) is applied over the data to drastically reduce the dimension of the feature space, followed by the study of neural networks and support vector machines (SVM) classifiers. The achieved accuracy results reach 98.33% and 93.41% for PET and SPECT respectively, which means a significant improvement over the results obtained by the classical Voxels-As-Features (VAF) reference approach.
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| 18 |
,Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis, |
J. Ramírez,R. Chaves,J. M. Górriz,I. Álvarez,M. López,D. Salas-Gonzalez,F. Segovia |
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Abstract
Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. As the number of AD patients has increased, its early diagnosis has received more attention for both social and medical reasons. Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper evaluates different pattern classifiers including .-nearest neighbor (.NN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis (CAD) system for improving the early detection of the AD. The proposed system, yielding a 98.7% AD diagnosis accuracy, reports clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.
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| 19 |
,Quality Checking of Medical Guidelines Using Interval Temporal Logics: A Case-Study, |
Guido Sciavicco,Jose M. Juarez,Manuel Campos |
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Abstract
Computer-based decision support in health-care is becoming more and more important in recent years. . are documents supporting health-care professionals in managing a disease in a patient, in order to avoid non-standard practices or outcomes. In this paper, we consider the problem of formalizing a guideline in a logical language. The target language is an interval-based temporal logic interpreted over natural numbers, namely the Propositional Neighborhood Logic, which has been shown to be expressive enough for our objective, and for which the satisfiability problem has been shown to be decidable. A case-study of a real guideline is presented.
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| 20 |
,Classification of SPECT Images Using Clustering Techniques Revisited, |
J. M. Górriz,J. Ramírez,A. Lassl,I. Álvarez,F. Segovia,D. Salas,M. López |
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Abstract
We present a novel classification method of SPECT images based on clustering for the diagnosis of Alzheimer’s disease. The aims of the clustering approach which is based on Gaussian Mixture Model (GMM) for density estimation, is to automatically select Regions of Interest (ROIs) and to effectively reduce the dimensionality of the problem. The clusters represented by Gaussians are constructed according to a maximum likelihood criterion employing the expectation maximization (EM) algorithm. By considering only the intensity levels inside the clusters, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features. With this feature extraction method one avoids the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the clustering method yields higher accuracy rates than the classification considering all voxel values.
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