Histopathological Image Analysis: A Review <s> C. Exploratory Histopathology Image Analysis <s> 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. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> C. Exploratory Histopathology Image Analysis <s> The histological variability of Glioblastomas (GB) precludes the modern assimilation of theses tumors into a single histological tumor group. As an alternative to statistical histological evaluation, we investigated 1489 human GB in order to discover whether they could be correctly classified using Self-Organizing Maps (SOM). In all tumors 50 histological features, as well as the age and sex of the patients, were examined. Four clusters of GB with a significance of 52 (maximal significance 60) were found. Cluster C1 contained 37.47% of all GB and 41.09% of all polymorphic glioblastomas (PG). Cluster C2 included 35.06% of all GB and 44.96% of all giant cell glioblastomas (GCG). Cluster C3 contained 16.45% of all GB with a significant component of astroblasts, glioblasts and oligodendroglia. Cluster C4 included 11.01% of all GB, 87.80% of the gliosarcomas (GS) and 36.72% of all GCG. Placing a series of component windows with their maps side by side allows the immediate recognition of the dependencies on variables and the determination of variables necessary to build the specific clusters. The SOM allow a realistic histological classification, comparable to the actual classification by the WHO. In addition, we found new, small subclusters of human GB which may have a clinical significance. With SOM one can learn to discriminate, discard and delete data, select histological and clinical or genetic variables that are meaningful, and consequently influence the result of patient management. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> C. Exploratory Histopathology Image Analysis <s> With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of analysis can identify biologically distinct disease subclasses of breast cancer with distinct molecular signatures, differential recurrence rates and potentially, very different response to therapy. Image analysis methods are also rapidly finding application in the clinic, complementing the pathologist in quantitative, reproducible, detection, staging, and grading of disease. We will describe novel computerized image analysis techniques and machine learning tools for automated cancer detection from digitized histopathology and how they can be employed for disease diagnosis and prognosis for prostate and breast cancer. <s> BIB003
Exploratory analysis of histopathology images can help in finding salient diagnostic features used by humans, associating them with the computed features, and visualizing relationships between different features in high-dimensional spaces. Lessmann et al. BIB001 have proposed the use of self-organizing maps (SOMs) for exploratory analysis of their wavelet-based feature space. The SOM-based visualization of the feature space allowed the authors of BIB001 to establish a correlation between single features and histologically relevant image structures, making the selection of a subset of clinically important features possible. Iglesias-Rozas and Hopf BIB002 showed that SOMs can be effectively employed to correctly classify different subtypes of human Glioblastomas (GB) and also to select significant histological and clinical or genetic variables. Alternatively, dimensionality reduction methods may offer a way of looking at trends and patterns in the data in a reduced dimensional space  - BIB003 .