Horst Bischof

Multispectral classification of Landsat-images using neural networks

The authors report the application of three-layer back-propagation networks for classification of Landsat TM data on a pixel-by-pixel basis. The results are compared to Gaussian maximum likelihood classification. First, it is shown that the neural network is able to perform better than the maximum likelihood classifier. Secondly, in an extension of the basic network architecture it is shown that textural information can be integrated into the neural network classifier without the explicit definition of a texture measure. Finally, the use of neural networks for postclassification smoothing is examined. >

Sparse MRF Appearance Models for Fast Anatomical Structure Localisation

Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

Automatic Quantification of Joint Space Narrowing and Erosions in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic disease that affects and potentially destroys the joints of the appendicular skeleton. The precise and reproducible quantification of the progression of joint space narrowing and the erosive bone destructions caused by RA is crucial during treatment and in imaging biomarkers in clinical trials. Current manual scoring methods exhibit high interreader variability, even after intensive training, and thus, impede the efficient monitoring of the disease. We propose a fully automatic quantitative assessment of the radiographic changes that result from RA, to increase the accuracy, reproducibility, and speed of image interpretation. Initial joint location estimates are obtained by local linear mappings based on texture features. Bone contours are delineated by active shape models comprised of statistical models of bone shape and local texture. These models are refined by snakes which increase the accuracy and allow for a fitting of pathological deviations from the training population. The method then measures joint space widths and detects erosions on the bone contour. Joint space widths are measured with a coefficient of variation of 2%-7% for repeated measurements and erosion detection exhibits an area under the receiver operating characteristic (ROC) curve of 0.89. Model landmarks serve as a reference system along the contour. These landmarks enable the definition of joint regions and more specific follow-up monitoring. The automatic quantification allows for a remote analysis, relevant for multicenter clinical trials, and reduces the workload of clinical experts since parts of the process can be managed by nonexpert personnel.

Human tracking by mode seeking

Change detection by background subtraction is a common approach to detect moving foreground. The resulting difference image is usually thresholded to obtain objects based on pixel connectedness and resulting blob objects are subsequently tracked. This paper proposes a detection approach not requiring the binarization of the difference image. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. Object tracking is carried out by updating and propagating cluster parameters over time using the mode seeking property of the mean shift procedure. For occluding targets, a fast procedure determining the object configuration maximizing image likelihood is presented. Detection and tracking results are demonstrated for a crowded scene and evaluation of the proposed tracking framework is presented.

Visualization methods for neural networks

The interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in the network. The authors propose a framework for visualization methods suited for feed forward neural networks. The basic idea is to use the spatial information available outside the network to arrange the data to be visualized (weights, activations of units) in the spatial domain of the display. Several examples which illustrate the proposed framework are presented.<<ETX>>

Verwendung von neuralen Netzwerken zur Klassifikation natürlicher Objekte am Beispiel der Baumerkennung aus Farb-Infrarot-Luftbildern

Es wird die Verwendung von neuralen Netzwerken zur Klassifikation von naturlichen Objekten erortert, als Beispiel dient die Baumartenbestimmung von Baumen auf Farb-Infrarot-Luftbildern. Es wird gezeigt, wie die Vorhersagegenauigkeit durch das Zusammenbauen von Netzwerken, die mit verschiedenen Parametern trainiert wurden, gesteigert werden kann, wobei WV-Diagramme (weight visualization diagrams) ein wertvolles Hilfsmittel darstellen. Weiters wird die Einbindung von neuralen Netzwerken in konventionelle bildverstehende Systeme diskutiert.