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Biomedical Image Segmentation and Analysis using ML and CI Techniques (1st CIBio-CBIC)
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#biomedicalimage #computationalintelligence #biomathematics
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Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to advancements in imaging acquisition modalities and High Throughput technologies [Peng 2008, Shamir 2010]. This huge information ensemble could overwhelm analytic capabilities concerning both physicians in their decision-making tasks and biologists in investigating complex biological systems [Meijering 2016]. With particular reference to biomedical images, medical imaging comprises minimally invasive techniques for acquiring biomedical images that provide detailed information about the anatomy and physiology of the imaged organs [Duncan 2000], while live-cell microscopy imaging allows for the visualization and analysis of living specimens’ dynamic processes [Meijering 2012].
Quantitative imaging methods provide scientifically and clinically relevant data in prediction, prognostication or response assessment [Yankeelov 2016], by also exploiting radiomics approaches [Aerts 2014]. In this regard, Computational Intelligence and Machine Learning can significantly improve traditional image processing techniques [Wang 2012]. Therefore, computational approaches for medical and biological image analysis play a key role in radiology and laboratory applications [Wernick 2010]. However, conventional Machine Learning and Computational techniques must be adapted and tailored to address the unique challenges regarding biomedical images [Kraus 2017, Shen 2017].
In this talk, the challenges and the characteristics of the most recent methods will be introduced and discussed by means of several practical applications, focusing on image segmentation and registration in cancer imaging [Brady 2016]. As a matter of fact, medical image segmentation, which consists in detecting and delineating Regions of Interests (e.g., organs or pathological areas), is one of the most critical clinical tasks. Novel methods for computer-assisted Magnetic Resonance image segmentation, mainly based on unsupervised Fuzzy C-Means clustering techniques, will be presented [Militello 2015, Rundo 2017b, Rundo 2017c]. In the context of multispectral and multimodal image processing [Rundo 2017a, Rundo 2017d], image registration represents a fundamental step because it enables to integrate different images into a single representation (i.e., the same reference system) [Maes 1997]. In particular, biomedical image registration approaches using Particle Swarm Optimization [Kennedy 1995] will be investigated [Wachowiak 2004, Rundo 2016]. To conclude, a recent medical image enhancement method based on Genetic Algorithms [Holland 1992] will be briefly described.
Help us caption & translate this video!
Note. I would like to thank all the speaker for kindly allowing their webinar to be here :)
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Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to advancements in imaging acquisition modalities and High Throughput technologies [Peng 2008, Shamir 2010]. This huge information ensemble could overwhelm analytic capabilities concerning both physicians in their decision-making tasks and biologists in investigating complex biological systems [Meijering 2016]. With particular reference to biomedical images, medical imaging comprises minimally invasive techniques for acquiring biomedical images that provide detailed information about the anatomy and physiology of the imaged organs [Duncan 2000], while live-cell microscopy imaging allows for the visualization and analysis of living specimens’ dynamic processes [Meijering 2012].
Quantitative imaging methods provide scientifically and clinically relevant data in prediction, prognostication or response assessment [Yankeelov 2016], by also exploiting radiomics approaches [Aerts 2014]. In this regard, Computational Intelligence and Machine Learning can significantly improve traditional image processing techniques [Wang 2012]. Therefore, computational approaches for medical and biological image analysis play a key role in radiology and laboratory applications [Wernick 2010]. However, conventional Machine Learning and Computational techniques must be adapted and tailored to address the unique challenges regarding biomedical images [Kraus 2017, Shen 2017].
In this talk, the challenges and the characteristics of the most recent methods will be introduced and discussed by means of several practical applications, focusing on image segmentation and registration in cancer imaging [Brady 2016]. As a matter of fact, medical image segmentation, which consists in detecting and delineating Regions of Interests (e.g., organs or pathological areas), is one of the most critical clinical tasks. Novel methods for computer-assisted Magnetic Resonance image segmentation, mainly based on unsupervised Fuzzy C-Means clustering techniques, will be presented [Militello 2015, Rundo 2017b, Rundo 2017c]. In the context of multispectral and multimodal image processing [Rundo 2017a, Rundo 2017d], image registration represents a fundamental step because it enables to integrate different images into a single representation (i.e., the same reference system) [Maes 1997]. In particular, biomedical image registration approaches using Particle Swarm Optimization [Kennedy 1995] will be investigated [Wachowiak 2004, Rundo 2016]. To conclude, a recent medical image enhancement method based on Genetic Algorithms [Holland 1992] will be briefly described.
Help us caption & translate this video!
Note. I would like to thank all the speaker for kindly allowing their webinar to be here :)
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