Birgit Lessmann

A method for linking computed image features to histological semantics in neuropathology

In medical image analysis the image content is often represented by features computed from the pixel matrix in order to support the development of improved clinical diagnosis systems. These features need to be interpreted and understood at a clinical level of understanding Many features are of abstract nature, as for instance features derived from a wavelet transform. The interpretation and analysis of such features are difficult. This lack of coincidence between computed features and their meaning for a user in a given situation is commonly referred to as the semantic gap. In this work, we propose a method for feature analysis and interpretation based on the simultaneous visualization of feature and image domain. Histopathological images of meningiomas WHO (World Health Organization) grade I are represented by features derived from color transforms and the Discrete Wavelet Transform. The wavelet-based feature space is then visualized and explored using unsupervised machine learning methods. We show how to analyze and select features according to their relevance for the description of clinically relevant characteristics.

Feature-space exploration of pathology images using content-based database visualization

In this work we present a method for the interactive feature space exploration and content-based database visualization (CBDV) of medical image databases. Using Self Organizing Maps it is possible to visualize the content of a medical image database. This visualization provides the basis for an interactive visual exploration of the meaning of image features and a characterization of the database content.

SOM-Based wavelet filtering for the exploration of medical images

In medical image analysis there are many applications that require the definition of characteristic image features. Especially computationally generated characteristic image features have potential for the exploration of large datasets. In this work, we propose a method for investigating time series of medical images using a combination of the Discrete Wavelet Transform and the Self Organizing Map. Our approach allows relevant image information to be identified in wavelet space. This enables us to develop a filter algorithm suitable to find and extract the characteristic image features and to suppress interfering non-relevant image information.

Visual Exploration of Pathology Images by a Discrete Wavelet Transform Preprocessed Locally Linear Embedding

The information content of large collections of histopathological images can be explored utilizing computer-based techniques that can help the user to explore the similarity between different brain tumor types. To visually inspect the degree of similarity between different tumors, we propose a combined approach based on the Discrete Wavelet Transform (DWT) and Locally Linear Embedding (LLE). The former is employed as a preprocessing utility, the latter achieves the dimensional reduction required for visualization.

Content Based Image Retrieval for Dynamic Time Series Data

Content based image retrieval (CBIR) systems in the field of medical image analysis are an active field of research. They allow the user to compare a given case with others in order to assist in the diagnostic process. In this work a CBIR system is described working on datasets which are both time- and space-dependent. Different possible feature sets are investigated, in order to explore how these datasets are optimally represented in the corresponding database.