Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Multimodal image-guided interventions using oncological biomarkers

This thesis consists of two parts addressing novel imaging technologies to improve the treatment of cancer patients. In part I, the additional value of real time image guidance during surgery is discussed and the research described in this part of the thesis showed that imaging performed during surgery can be of great value. Nevertheless, the success rate is highly dependent on the choice of imaging modality and biomarker to be targeted. In part II, a necrosis avid probe was successfully evaluated as novel method for early neoadjuvant treatment response monitoring.department of Radiology iThera Medical GmbH MiLabs ChipsoftLUMC / Geneeskund

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions