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154 results on '"Ahammer H"'

1. The Spatial‐Temporal Dimension of Oncological Prevalence and Mortality in Romania

Author

Peptenatu, D., primary, Nedelcu, I. D., additional, Pop, C. S., additional, Simion, A. G., additional, Furtunescu, F., additional, Burcea, M., additional, Andronache, I., additional, Radulovic, M., additional, Jelinek, H. F., additional, Ahammer, H., additional, Gruia, A. K., additional, Grecu, A., additional, Popa, M. C., additional, Militaru, V., additional, Drăghici, C. C., additional, and Pintilii, R. D., additional

Published
2023
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17. Dynamics of forest fragmentation and connectivity using particle and fractal analysis

Author

Andronache, I., Marin, M., Fischer, Rico, Ahammer, H., Radulovic, M., Ciobotaru, A.-M., Jelinek, H.F., Di Ieva, A., Pintilii, R.-D., Drăghici, C.-C., Herman, G.V., Nicula, A.-S., Simion, A.-G., Loghin, I.-V., Diaconu, D.-C., Peptenatu, D., Andronache, I., Marin, M., Fischer, Rico, Ahammer, H., Radulovic, M., Ciobotaru, A.-M., Jelinek, H.F., Di Ieva, A., Pintilii, R.-D., Drăghici, C.-C., Herman, G.V., Nicula, A.-S., Simion, A.-G., Loghin, I.-V., Diaconu, D.-C., and Peptenatu, D.

Abstract

The ever decreasing area of forests has lead to environmental and economical challenges and has brought with it a renewed interest in developing methodologies that quantify the extent of deforestation and reforestation. In this study we analyzed the deforested areas of the Apuseni Mountains, which has been under economic pressure in recent years and resulted in widespread deforestation as a means of income. Deforested surface dynamics modeling was based on images contained in the Global Forest Database, provided by the Department of Geographical Sciences at Maryland University between 2000 and 2014. The results of the image particle analysis and modelling were based on Total Area (ha), Count of patches and Average Size whereas deforested area distribution was based on the Local Connected Fractal Dimension, Fractal Fragmentation Index and Tug-of-War Lacunarity as indicators of forest fragmentation or heterogeneity. The major findings of the study indicated a reduction of the tree cover area by 3.8%, an increase in fragmentation of 17.7% and an increase in heterogeneity by 29%, while fractal connectivity decreased only by 0.1%. The fractal and particle analysis showed a clustering of forest loss areas with an average increase from 1.1 to 3.0 ha per loss site per year. In conclusion, the fractal and particle analysis provide a relevant methodological framework to further our understanding of the spatial effects of economic pressure on forestry.

Published
2019

19. Analysis of anal intraepithelial neoplasia images using 1d and 2d higuchi's fractal dimension methods

Author

Klonowski, Wlodzimierz, Stepien, P., Stepien, R., Sedivy, R., Ahammer, H., Spasić, Slađana, Klonowski, Wlodzimierz, Stepien, P., Stepien, R., Sedivy, R., Ahammer, H., and Spasić, Slađana

Abstract

The ILF (Image Landscapes' Fractal Dimension) method and DF2d method obtained by a 2D generalization of Higuchi's algorithm were applied to a set of 120 digital histological images of Anal Intraepithelial Neoplasia (AIN). The main goal of this research was to examine accuracy that means sensitivity and specificity of these methods and compare the applicability of both methods in the quantitative characterization and differentiation of clinical cases of AIN. Histological examination by an experienced pathologist revealed three grades of AIN tumors in the 120 histological slices: 36 of AIN1, 56 of AIN2 and 28 of AIN3. Statistical tests showed significant differences between calculated fractal dimension values in three datasets (AIN1, AIN2 and AIN3) using ILF and DF2d methods at the level of significance of 0.05. Application of the ILF and DF2d methods has an advantage when it comes to speed, accuracy, simplicity and time necessary for analysis. Both methods can be successfully applied for differentiation between AIN stages giving practically the same results. They can easily be adapted to other histological specimen.

Published
2018

23. Three−dimensional models of individual cardiac histoanatomy: Tools and challenges

Author

Burton, R, Plank, G, Schneider, J, Grau, V, Ahammer, H, Keeling, S, Lee, J, Smith, N, Gavaghan, D, Trayanova, N, and Kohl, P

Abstract

There is a need for, and utility in, the acquisition of data sets of cardiac histoanatomy, with the vision of reconstructing individual hearts on the basis of noninvasive imaging, such as MRI, enriched by reference to detailed atlases of serial histology obtained from representative samples. These data sets would be useful not only as a repository of knowledge regarding the specifics of cardiac histoanatomy, but could form the basis for generation of individualized high-resolution cardiac structure-function models. The current article presents a step in this general direction: it illustrates how whole-heart noninvasive imaging can be combined with whole-heart histology in an approach to achieve automated construction of histoanatomically detailed models of cardiac 3D structure and function at hitherto unprecedented resolution and accuracy (based on 26.4 x 26.4 x 24.4 microm MRI voxel size, and enriched by histological detail). It provides an overview of the tools used in this quest and outlines challenges posed by the approach in the light of applications that may benefit from the availability of such data and tools.

Published
2016

31. German Antarctic Operations

Author

Gernandt, Hartwig, Matz, Thomas, Ahammer, H., Janneck, Jürgen, Krause, Reinhard, Drücker, Cord, Gernandt, Hartwig, Matz, Thomas, Ahammer, H., Janneck, Jürgen, Krause, Reinhard, and Drücker, Cord

Published
2000

41. Learning discriminative classification models for grading anal intraepithelial neoplasia

Author

Kainz Philipp, Mayrhofer-Reinhartshuber Michael, Sedivy Roland, and Ahammer Helmut

Subjects
digital pathology, fractal analysis, histopathological image analysis, machine learning, support vector machines, tissue grading, Medicine
Abstract

Grading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter- and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading strategy yields highly reliable results. Compared to existing methods, the proposed method outperformed them by a significant margin.

Published
2016
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42. Improvements on coronal hole detection in SDO/AIA images using supervised classification

Author

Reiss Martin A., Hofmeister Stefan J., De Visscher Ruben, Temmer Manuela, Veronig Astrid M., Delouille Véronique, Mampaey Benjamin, and Ahammer Helmut

Subjects
Solar wind, Coronal holes, Filament channels, Feature extraction, Supervised Classification, Textural features, Meteorology. Climatology, QC851-999
Abstract

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011–2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine (SVM), Linear Support Vector Machine, Decision Tree, and Random Forest, and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ≈ 0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.

Published
2015
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