Sara Colantonio

A Knowledge Editing Service for Multisource Data Management in Remote Health Monitoring

Remote health monitoring (RHM) programmes are being increasingly developed to face the pervasive diffusion of chronic diseases. RHM strongly relies on Information and Communications Technologies (ICT) intelligent platforms devised to remotely acquire multisource data, process these according to specific domain knowledge, and support clinical decision making. However, since RHM domain is continuously evolving and the pertinent knowledge is not yet consolidated, there is a great demand for services and tools that allow the encoded knowledge to be modified and enriched. This paper presents a knowledge editing service (KES), which aims at enabling clinicians to insert novel knowledge, in a controlled fashion, into an ICT intelligent platform. The solution proposed is innovative since it addresses synergistically peculiar issues related to 1) RHM knowledge format; 2) controlled editing patterns; 3) knowledge verification; and 4) cooperative knowledge editing. None of the existing methods and systems for knowledge authoring tackles all these aspects at the same time. A prototype of the KES has been implemented and evaluated in real operational conditions.

Thesaurus project: design of new autonomous underwater vehicles for documentation and protection of underwater archaeological sites

The Thesaurus Project, funded by the Regione Toscana, combines humanistic and technological research aiming at developing a new generation of cooperating Autonomous Underwater Vehicles and at documenting ancient and modern Tuscany shipwrecks. Technological research will allow performing an archaeological exploration mission through the use of a swarm of autonomous, smart and self-organizing underwater vehicles. Using acoustic communications, these vehicles will be able to exchange each other data related to the state of the exploration and then to adapt their behavior to improve the survey. The archival research and archaeological survey aim at collecting all reports related to the underwater evidences and the events of sinking occurred in the sea of Tuscany. The collected data will be organized in a specific database suitably modeled.

A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination

The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.

Face morphology

This paper proposes a method for an automatic extraction of geometric features, related to weight parameters, from 3D facial data acquired with low-cost depth scanners. The novelty of the method relies both on the processing of the 3D facial data and on the definition of the geometric features which are conceptually simple, robust against noise and pose estimation errors, computationally efficient, invariant with respect to rotation, translation, and scale changes. Experimental results show that these measurements are highly correlated with weight, BMI, and neck circumference, and well correlated with waist and hip circumference, which are markers of central obesity. Therefore the proposed method strongly supports the development of interactive, non obtrusive systems able to provide a support for the detection of weight-related problems.

Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications

Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Naive Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the perform-ance to decision trees and Naive Bayesian. Finally, we give an outlook for further work.

Biomedical Signal and Image Processing for Decision Support in Heart Failure

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

Cell image analysis ontology

Cell image analysis in microscopy is the core activity of cytology and cytopathology for assessing cell physiological (cellular structure and function) and pathological properties. Biologists usually make evaluations by visually and qualitatively inspecting microscopic images: this way, they are particularly able to recognize deviations from normality. Nevertheless, automated analysis is strongly preferable for obtaining objective, quantitative, detailed, and reproducible measurements, i.e., features, of cells. Yet, the organization and standardization of the wide domain of features used in cytometry is still a matter of challenging research. In this paper, we present the Cell Image Analysis Ontology (CIAO), which we are developing for structuring the cell image features domain. CIAO is a structured ontology that relates different cell parts or whole cells, microscopic images, and cytometric features. Such an ontology has incalculable value since it could be used for standardizing cell image analysis terminology and features definition. It could also be suitably integrated into the development of tools for supporting biologists and clinicians in their analysis processes and for implementing automated diagnostic systems. Thus, we also present a tool developed for using CIAO in the diagnosis of hematopoietic diseases.

Automatic fuzzy-neural based segmentation of microscopic cell images

In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for developing an automated application for the early diagnostics of lymphatic system tumours. The proposed method follows a two-step approach to, firstly, find the nuclei and then, to refine the segmentation by means of a neural model, capable of localising the borders of each nucleus. Experimental results have shown the feasibility of the method.

Heart deformation pattern analysis through shape modelling

In this paper, we present an approach to the description of time-varying anatomical structures. The main goal is to compactly but faithfully describe the whole heart cycle in such a way to allow for deformation pattern characterization and assessment. Using such an encoding, a reference database can be built, thus permitting similarity searches or data mining procedures.