New Developments in Biomedical Engineering

ing and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. At the beginning of the chapter, the quality assessment for fundus images was defined as "the characteristics of an image that allow the retinopathy diagnosis by a human or software expert". The literature was surveyed to find techniques which could help to achieve this goal. General image QA does not seem well suited for our purposes, as they are mainly dedicated to the detection of artefacts due to compression and they often require the original non-degraded image, something that does not make much sense in the context of QA for retinal images. Our survey found five publications which tackled a problem comparable to the one of this project. They were divided into 3 categories: "Histogram Based", "Retina Morphology" and "Bag-of-Words". The authors of the first category approached the problem by computing relatively simple features and comparing them to a model of a good quality image. Although this approach might have advantages like speed and ease of training, it does not generalise well on the natural variability of fundus images as highlighted by Niemeijer et al. (2006) and Fleming et al. (2006). "Retina Morphology" methods started to take into account features unique to the retina, such as vessels, optic nerve or temporal arcades. This type approach considerably increased the QA accuracy. Remarkably, Fleming et al. developed a very precise way to judge the quality of image clarity and field definition which closely resembled what an ophthalmologist would do. The main drawbacks are time required to locate the various structures and the fact that if the image quality is too poor, some of the processing steps might fail, giving unpredictable results. This is unlikely to happen in the problem domain of Fleming et al. because they worked with images taken by trained ophthalmologists, but this is not the case with systems that can be used by personnel with basic training. The only method of the "Bag-of-Words" category is the one developed by Niemeijer et al. Their technique is based on pattern recognition algorithms which gave high accuracy and specificity. The main drawback is again speed of execution. The new approach described in this chapter was partially inspired by all these techniques: colour was used as features as in the "Histogram Based" technique, the vessels were segmented as a preprocessing step like in the "Retina Morphology" techniques and the QA was computed by a classifier similar to the one used in the "Bag-of-Words" techniques. New features were developed and used such as ELVD, VFOV and the use of the HSV colour space, which was not evaluated by any of the previous authors for QA of fundus images. This made possible the creation of a method capable of classifying the quality of an image with a score from 0 to 1 in a period of time much shorter than "Retina Morphology" and "Bag-of-Words" techniques. Features, classifier types and other parameters were selected based on the results of empirical tests. Four different types of datasets were used. Although none are very large (none contained more than 100 images) they were fairly good representative of the variation of fundus images in terms of quality, camera used and patient's ethnicity. In the literature, the method which seemed to perform best and which had the best generalisation was the one of Niemeijer et al. It was implemented and compared to our algorithm. Our results are in favour of the Preface......Page 5 Mihalis G. Markakis, Georgios D. Mitsis, George P. Papavassilopoulos and Vasilis Z. Marmarelis......Page 11 Stefanos Georgiadis, Perttu Ranta-aho, Mika Tarvainen and Pasi Karjalainen......Page 31 Carlos S. Lima, Adriano Tavares, José H. Correia, Manuel J. Cardoso and Daniel Barbosa......Page 47 Aleksandar Jeremic......Page 83 Tiago H. Falk, Wai-Yip Chan, Ervin Sejdić and Tom Chau......Page 103 Yuan-yuan Su, Zhen-ji Li, and Tao Wang......Page 115 Damien Coyle......Page 133 Youssef Belhamadia......Page 161 Saurabh Prasad, Lori M. Bruce and John E. Ball......Page 173 Hitoshi Iyatomi......Page 193 Luca Giancardo, Fabrice Meriaudeau, Thomas P Karnowski, Dr Edward Chaum and Kenneth Tobin......Page 211 Jinghao Zhou, Sukmoon Chang, Dimitris Metaxas and Gig Mageras......Page 235 Gert Wollny, María J. Ledesma-Carbayo, Peter Kellman and Andrés Santos......Page 245 Tae-Seong Kim and Md. Zia Uddin......Page 259 Alberto Yúfera and Adoración Rueda......Page 273 Y. Ye-Lin, J. Garcia-Casado, Jose-M. Bueno-Barrachina, J. Guimera Tomas, G. Prats-Boluda and J.L. Martinez de Juan......Page 297 Dries Braeken and Dimiter Prodanov......Page 321 Lioudmila Tchvialeva, Haishan Zeng, Igor Markhvida, David I McLean, Harvey Lui and Tim K Lee......Page 351 Yupeng Ren, Hyung-Soon Park, Yi-Ning Wu, François Geiger and Li-Qun Zhang......Page 369 Keisuke Shima, Toshio Tsuji, Akihiko Kandori, Masaru Yokoe and Saburo Sakoda......Page 383 Josep Solà, Stefano F. Rimoldi, Yves Allemann......Page 401 Audrius Brazdeikis and Nikhil S. Padhye......Page 435 Renato E. de Araujo, Diego J. Rativa, Marco A. B. Rodrigues, Armando Marsden and Luiz G. Souza Filho......Page 457 Jianhua Zhao, Harvey Lui, David I. McLean and Haishan Zeng......Page 465 Tung-Chien Chen, Kuanfu Chen, Wentai Liu and Liang-Gee Chen......Page 485 Aimé Lay-Ekuakille and Amerigo Trotta......Page 501 Shuichi Ino and Mitsuru Sato......Page 509 Mario-Ibrahín Gutiérrez, Arturo Vera and Lorenzo Leija......Page 527 Artur Turek and Beata Cwalina......Page 553 Yasutoshi Ishihara, Naoki Wadamori and Hiroshi Ohwada......Page 579 Jamil. Y. Khan and Mehmet R. Yuce......Page 601 Johannes Thiele, Jó Ágila Bitsch Link, Okuary Osechas, Hanspeter Mallot and Klaus Wehrle......Page 639 Michel Vacher, Anthony Fleury, François Portet, Jean-François Serignat and Norbert Noury......Page 655 Luca Piccini, Oriana Ciani and Giuseppe Andreoni......Page 685 Domenico Campolo, Fabrizio Taffoni, Giuseppina Schiavone, Domenico Formica, Eugenio Guglielmelli and Flavio Keller......Page 695 Biomedical Engineering is a highly interdisciplinary and well established discipline spanning across Engineering, Medicine and Biology. A single definition of Biomedical Engineering is hardly unanimously accepted but it is often easier to identify what activities are included in it. This volume collects works on recent advances in Biomedical Engineering and provides a bird-view on a very broad field, ranging from purely theoretical frameworks to clinical applications and from diagnosis to treatment. The 35 chapters composing this book can be grouped into five major domains: I. Modeling: chapters 1 - 4 propose advanced approaches to model physiological phenomena which are, in general, nonlinear, non-stationary and non-deterministic; II. Data Analysis: chapters 5 - 14 relate to the analysis and processing of data which originate from the human body and which incorporate spatial or temporal patterns indicative for diagnostic purposes; III. Physiological Measurements: chapters 15 - 24 describe a variety of biophysical methods for assessing physiological functions, for use in research as well as in clinical practice; IV. Biomedical Devices and Materials: chapters 25 - 30 highlight aspects behind design and characterization of biomedical instruments which include electromechanical transduction and control; V. Recent Approaches to Behavioral Analysis: finally, chapters 31 - 35 propose recent and novel approaches to the analysis of behavior in humans and animal models, with emphasis on home-care delivery and monitoring. This book is meant to provide a small but valuable sample of contemporary research activities around the world in the field of Biomedical Engineering and is expected to be useful to a large number of researchers in different biomedical fields. EP research has to deal with several inherent difficulties. Traditional analysis is based on averaged data often by forming extra grand averages of different populations. Thus, trial-to-trial variability and individual subject characteristics are largely ignored (Fell, 2007). Therefore, the study of isolated components retrieved by averages might be misleading, or at least it is a simplification of the reality. For example, habituation may occur and the responses could be different from the beginning to the end of the recording session. Furthermore, cognitive potentials exhibit rich latency and amplitude variability that traditional research based on averaging is not able to exploit for studying complex cognitive processes. Latency variability could be used, for instance, for studying perceptual changes, quantifying stimulus classification speed or task difficulty. In this chapter, state-space modeling for single-trial estimation of EPs was presented in its general form based on Bayesian estimation theory. This formulation enables the selection of different models for dynamical estimation. In general, the applicability of the proposed