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Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry

Third International Conference, MDA 2008 Leipzig, Germany, July 14, 2008 Proceedings

herausgegeben von: Petra Perner, Ovidio Salvetti

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The automatic analysis of signals and images together with the characterization and elaboration of their representation features is still a challenging activity in many relevant scientific and hi-tech fields such as medicine, biotechnology, and chemistry. Multidimensional and multisource signal processing can generate a number of information patterns which can be useful to increase the knowledge of several domains for solving complex problems. Furthermore, advanced signal and image manipulation allows relating specific application problems into pattern recognition problems, often implying also the development of KDD and other computational intelligence procedures. Nevertheless, the amount of data produced by sensors and equipments used in biomedicine, biotechnology and chemistry is usually quite huge and structured, thus strongly pushing the need of investigating advanced models and efficient computational algorithms for automating mass analysis procedures. Accordingly, signal and image understanding approaches able to generate automatically expected outputs become more and more essential, including novel conceptual approaches and system architectures. The purpose of this third edition of the International Conference on Mass Data Analysis of Signals and Images in Medicine, Biotechnology, Chemistry and Food Industry (MDA 2008; www.mda-signals.de) was to present the broad and growing scientific evidence linking mass data analysis with challenging problems in medicine, biotechnology and chemistry. Scientific and engineering experts convened at the workshop to present the current understanding of image and signal processing and interpretation methods useful for facing various medical and biological problems and exploring the applicability and effectiveness of advanced techniques as solutions.

Inhaltsverzeichnis

Frontmatter

New Models for Immune Mechanism Diagnosis

Abstract

In this paper we introduce a discrete event system model of immune systems of mammalians using Markov Decision Processes based on Petri Nets Models. Based on immunological principles we propose an approach in order to study the mechanisms that govern the immune system’s functionality. A two-module algorithm that launches a specific action against an anomalous situation is developed. The Petri nets tools are assumed in this approach. Also, Markov Decision Processes (MDPs) with a truncated state space to the problem with infinite state space is considered. We also propose a new algorithm to build a large model (e.g., a macro-model) of immune mechanisms of mammalians. We show that an optimal stationary policy exists and we apply the results of [1] to a dynamic scheduling problem of the immunological response to external stimuli.

Calin Ciufudean, Otilia Ciufudean, Constantin Filote

User Assisted Substructure Extraction in Molecular Data Mining

Abstract

In molecular fragments mining, scientists use both manual techniques and pure computer based methods. In this paper, we propose a novel molecular fragment mining approach that incorporates interactive user assistance to speed up and increase the success rates in traditional fragment mining processes. The proposed approach visualizes 3D molecular data in 2D form that can be easily interpreted by a human expert who evaluates and filters the 2D molecular images manually. The proposed approach differs from others in literature as it does not search substructures including specific atoms like graph mining methods do. Instead, user assisted approach highlights significant substructures with specific properties and topologies graphically. Initial experiments indicate that by the use of user assisted approach, active and inactive fragments of compounds are quickly determined for drug design with high success rates.

Burcu Yılmaz, Mehmet Göktürk, Natalie Shvets

Fully Automatic Heart Beat Rate Determination in Digital Video Recordings of Rat Embryos

Abstract

Embryo cultures of rodents is an established technique for monitoring adverse effects of chemicals on embryonic development. The assessment involves determination of the heart rate of the embryo which is usually done visually, a technique which is tedious and error prone. We present a new method for fully automatic heart detection in digital videos of rat embryos. First it detects the heart location, and then it counts the number of heart beats for a predetermined period of time. Using this automated method many more embryos can be evaluated at reasonable cost.

M. Khalid Khan, Mats F. Nilsson, Bengt R. Danielsson, Ewert Bengtsson

Biomedical Signal and Image Processing for Decision Support in Heart Failure

Abstract

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 base of the CDSS.

Franco Chiarugi, Sara Colantonio, Dimitra Emmanouilidou, Davide Moroni, Ovidio Salvetti

Automatic Data Acquisition and Signal Processing in the Field of Virology

Abstract

This paper presents an original experimental optical device (design and construction), based on the Light Scattering Spectroscopy (LSS) and microscopically control of the investigated field. The optical device used for automatic data acquisition in conjunction with a data processing module and fractal analysis. A light beam distributions mathematical model for experimental system is presented. Fractal analysis for Mie light scattering signals are used to extract information about cell nuclei size distributions and could be a useful tool to clearly discriminate between non-infected and virus infected cultures.

Radu Dobrescu, Loretta Ichim

Colorectal Polyps Detection Using Texture Features and Support Vector Machine

Abstract

In this paper we propose a novel method in detecting colorectal polyps on colonoscopic images. Texture features are applied in polyps and normal tissues training and classification. Support vector machine is used as a classifier to identify the position of polyps. Seventy-four colonoscopic images are collected to test the system. Half of them are used as training images and half are used as testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 mark per-image.

Da-Chuan Cheng, Wen-Chien Ting, Yung-Fu Chen, Qin Pu, Xiaoyi Jiang

OplAnalyzer: A Toolbox for MALDI-TOF Mass Spectrometry Data Analysis

Abstract

We present a software package for the analysis of MALDI-TOF mass spectrometry data. The software is designed to facilitate a complete exploratory workflow: pre-processing of raw spectral data, specification of study groups for comparison, statistical differential analysis, visualization of peptide peaks, and classification. The software supports various external tools for these tasks. We also pay special attention to the iterative nature of a typical analysis. Finally, we present two proteomics studies where the software has been used for data analysis.

Thang V. Pham, Connie R. Jimenez

Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching

Abstract

Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.

Tuan D. Pham, Honghui Wang, Xiaobo Zhou, Dominik Beck, Miriam Brandl, Gerard Hoehn, Joseph Azok, Marie-Luise Brennan, Stanley L. Hazen, Stephen T. C. Wong

A Mathematical Operator for Automatic and Real Time Analysis of Sequences of Vascular Images

Abstract

Due to the absolute value involved, the first absolute central moment can be divided into two complementary filters: a positive deviation e _p and a negative deviation e _n. Both e _p and e _n can be used separately to highlight edges, lines, line endings, corners and junctions in images. Furthermore, the recovered edge information can be usefully combined to obtain additional information that would not be obtained by varying the parameters of the original filter. The mass center of the first absolute central moment can be also defined and an iterative localization procedure can be developed by exploiting its properties. Mathematical operators derived from the first absolute central moment were used on a video processing device based on a DSP board and they proved to be robust and suitable for real-time implementations.

Marcello Demi, Elisabetta Bianchini, Francesco Faita, Vincenzo Gemignani

A Unified Mathematical Treatment of Regression Problems in Image Processing

Abstract

In this paper we study some optimization problems resulting from image processing tasks in medical applications. These problems are solved using a coordinate-free approach which not only reduces the computational effort in finding the solutions and enhances conceptual clarity, but also leads to closed-form solutions.

Karlheinz Spindler

Multi-scale Representation and Persistency for Shape Description

Abstract

Extraction, organization and exploitation of topological features are emerging topics in computer vision and graphics. However, such kind of features often exhibits weak robustness with respect to small perturbations and it is often unclear how to distinguish truly topological features from topological noise. In this paper, we present an introduction to persistence theory, which aims at analyzing multi-scale representations from a topological point of view. Besides, we extend the ideas of persistency to a more general setting by defining a set of discrete invariants.

Davide Moroni, Mario Salvetti, Ovidio Salvetti

Novel Computerized Methods in System Biology –Flexible High-Content Image Analysis and Interpretation System for Cell Images

Abstract

In the rapidly expanding fields of cellular and molecular biology, fluorescence illumination and observation is becoming one of the techniques of choice to study the localization and dynamics of proteins, organelles, and other cellular compartments, as well as a tracer of intracellular protein trafficking. The automatic analysis of these images and signals in medicine, biotechnology, and chemistry is a challenging and demanding field. Signal-producing procedures by microscopes, spectrometers and other sensors have found their way into wide fields of medicine, biotechnology, industrial and environmental analysis. With this arises the problem of the automatic mass analysis of signal information. Signal-interpreting systems which automatically generate the desired target statements from the signals are therefore of compelling necessity. The continuation of mass analysis on the basis of the classical procedures leads to investments of proportions that are not feasible. New procedures and system architectures are therefore required. We will present, based on our flexible image analysis and interpretation system Cell_Interpret, new intelligent and automatic image analysis and interpretation procedures. We will demonstrate it in the application of the HEp-2 cell pattern analysis.

Petra Perner

MDA 2006

Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

Abstract

The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells.

M. Tscherepanow, F. Zöllner, M. Hillebrand, F. Kummert

Backmatter

Titel: Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
herausgegeben von: Petra Perner
Ovidio Salvetti
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-540-70715-8
Print ISBN: 978-3-540-70714-1
DOI: https://doi.org/10.1007/978-3-540-70715-8