Fully automated biomedical image segmentation by self-organized model adaptation

Wismüller, Axel; Vietze, Frank; Behrends, Johannes; Meyer‐Baese, Anke; Reiser, Maximilian; Ritter, Helge

doi:10.1016/j.neunet.2004.06.015

Cited by 47 publications

(12 citation statements)

References 9 publications

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“…(Pratt, 2007), pre-processing (noise reduction, recoding, etc.) (Duda, 2000), (Shapiro, 2001), analysis (segmentation) of image (Gonzalez, 2008) (Duda, 2000) (Wismullera, 2004) and description (extraction of characteristic features) (Avtandilov, 1990) (Jahne, 2005) of micro-objects. One of the important stages of automation of analysis process of biomedical preparations is a selection of micro-objects (Avtandilov, 1990).…”

Section: Micro-objects Segmentation Taskmentioning

confidence: 99%

Modern Trends in Biomedical Image Analysis System Design

Berezsky¹,

Melnyk²,

Batko³

2011

Biomedical Engineering, Trends in Electronics, Communications and Software

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Section: Micro-objects Segmentation Taskmentioning

confidence: 99%

Modern Trends in Biomedical Image Analysis System Design

Berezsky¹,

Melnyk²,

Batko³

2011

Biomedical Engineering, Trends in Electronics, Communications and Software

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“…In addition, its application to the real-world problem of automatic nonlinear multispectral image registration, employing magnetic resonance imaging data sets of the human brain has been described in this paper, whereas the publications [61,[64][65][66] refer to the application of the DM algorithm to multispectral image segmentation, see Fig. 9.…”

Section: The Deformable Feature Mapmentioning

confidence: 99%

Computational intelligence in biomedical imaging: multidimensional analysis of spatio-temporal patterns

Wismüller

2010

Comput Sci Res Dev

Self Cite

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Technical innovations in radiology, such as advanced cross-sectional imaging methods, have opened up new vistas for the exploration of structure and function of the human body enabling both high spatial and temporal resolution. However, these techniques have led to vast amounts of data whose precise and reliable visual analysis by radiologists requires a considerable amount of human intervention and expertise, thus resulting in a cost factor of substantial economic relevance. Hence, the computer-assisted analysis of biomedical image data has moved into the focus of interest as an issue of high priority research efforts. In this context, innovative approaches to exploratory analysis of huge complex spatio-temporal patterns play a key role to improve computer-assisted signal and image processing in radiology. Examples of such approaches are various unsupervised vector quantization methods or supervised function approximation techniques, such as Generalized RadialBasis-Functions-(GRBF-) neural networks. Recent developments motivated by concepts of computational intelligence are the 'Deformable Feature Map' (DM) as an algorithm for self-organized model adaptation, the 'Mutual Connectivity Analysis' (MCA) as an instrument for the analysis of large time-series ensembles and the 'Exploratory Observation Machine' (XOM) as a novel general framework for learning by self-organization-three methods that the author has invented and applied to biomedical real-world applications. This contribution covers both conceptual foundations A. Wismüller ( )

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“…In addition, its application to the real-world problem of automatic nonlinear multispectral image registration, employing magnetic resonance imaging datasets of the human brain has been described there, whereas the publications [41], [39], [43], and [42] refer to the application of the DM algorithm to multispectral image segmentation; see Figure 7.12.…”

Section: Figure 712mentioning

confidence: 99%

Segmentation with Neural Networks

Wismueller

2009

Handbook of Medical Image Processing and Analysis

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Fully automated biomedical image segmentation by self-organized model adaptation

Cited by 47 publications

References 9 publications

Modern Trends in Biomedical Image Analysis System Design

Modern Trends in Biomedical Image Analysis System Design

Computational intelligence in biomedical imaging: multidimensional analysis of spatio-temporal patterns

Segmentation with Neural Networks

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