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This chapter considers the potential and flexibility of self-organizing tree map (SOTM) based and self-organizing hierarchical variance map (SOHVM) based learning for tasks in microbiological image analysis. As a demonstration of the SOHVM's ability to mine topological information from an input space, the chapter describes with an example for how such information can be used to simplify the task of visualizing a large three-dimensional (3D) stack of phase-contrast acquired plant chromosomes imaged during an advanced state of mitosis (cell division). The chapter considers two types of microbiological image data in order to demonstrate the potential for the proposed algorithm to achieve unsupervised, fully automatic segmentations. It shows examples of utilizing this automated property of the SOHVM to seek more natural segmentations of gray-level and higher order, multidimensional feature descriptions, with examples for the clustering of texture information and Local gray-level-based statistics.