Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transferfunction (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly inlow-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to thearea of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of localfrequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allowsus to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplifythe exploration step. We propose three approaches for data exploration which we illustrate with several visualization caseshighlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstratethe power of the method on selected datasets