Torfinn Taxt

Bio: Torfinn Taxt is an academic researcher from University of Bergen. The author has contributed to research in topics: Deconvolution & Blind deconvolution. The author has an hindex of 31, co-authored 95 publications receiving 5900 citations. Previous affiliations of Torfinn Taxt include Norwegian Computing Center & University of Oslo.

Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma

Abstract: Bevacizumab, an antibody against vascular endothelial growth factor (VEGF), is a promising, yet controversial, drug in human glioblastoma treatment (GBM). Its effects on tumor burden, recurrence, and vascular physiology are unclear. We therefore determined the tumor response to bevacizumab at the phenotypic, physiological, and molecular level in a clinically relevant intracranial GBM xenograft model derived from patient tumor spheroids. Using anatomical and physiological magnetic resonance imaging (MRI), we show that bevacizumab causes a strong decrease in contrast enhancement while having only a marginal effect on tumor growth. Interestingly, dynamic contrast-enhanced MRI revealed a significant reduction of the vascular supply, as evidenced by a decrease in intratumoral blood flow and volume and, at the morphological level, by a strong reduction of large- and medium-sized blood vessels. Electron microscopy revealed fewer mitochondria in the treated tumor cells. Importantly, this was accompanied by a 68% increase in infiltrating tumor cells in the brain parenchyma. At the molecular level we observed an increase in lactate and alanine metabolites, together with an induction of hypoxia-inducible factor 1α and an activation of the phosphatidyl-inositol-3-kinase pathway. These data strongly suggest that vascular remodeling induced by anti-VEGF treatment leads to a more hypoxic tumor microenvironment. This favors a metabolic change in the tumor cells toward glycolysis, which leads to enhanced tumor cell invasion into the normal brain. The present work underlines the need to combine anti-angiogenic treatment in GBMs with drugs targeting specific signaling or metabolic pathways linked to the glycolytic phenotype.

A Markov Random Field Model for Classificati of Multisource Satellite Imagery

Abstract: Abstruct- A general model for multisource classification of remotely sensed data based on Markov Random Fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (Geographic Information Systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GI§ crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GI§ field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov Random Field models for classification tasks and the encouraging experimental results in our small-scale study, we conclude that the proposed MRF model is useful for classification of multisource satellite imagery.

A Markov random field model for classification of multisource satellite imagery

Abstract: A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.

Evaluation of binarization methods for document images

Abstract: This paper presents an evaluation of eleven locally adaptive binarization methods for gray scale images with low contrast, variable background intensity and noise. Niblack's method (1986) with the addition of the postprocessing step of Yanowitz and Bruckstein's method (1989) added performed the best and was also one of the fastest binarization methods. >

Data clustering: a review

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

A survey of image classification methods and techniques for improving classification performance

Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

Multisensor image fusion in remote sensing: Concepts, methods and applications

Abstract: With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.