Somatosensory processing of duration and frequency changes was investigated using event-related potentials to vibrotactile stimuli. Intermittent vibration to the fingertips of either hand was presented using a two-stimulus odd-ball paradigm (deviant P = 0.10). One group (N = 12, 18-38 years) was presented with stimulus pairs of 20/70, 50/150 and 170/250 ms. A second group (N = 10, 19-34 years) was tested using frequency pairs of 200/70 Hz. A psychophysical study examined the subjects' ability to discriminate between different stimulus pairs. A clear negative shift in the response to the deviant stimulus was recorded with all the stimulus conditions used in both experiments. Both frequency changes and duration increments/decrements revealed an initial negativity in the subtraction waveform with a mean onset of 90-170 ms and a following positivity, both of which were dependent on the duration of the stimulus used. A significant decrease in the amplitude of both components was observed with the 170/250 ms pairing, coinciding with a positive correlation between individual discrimination performance and amplitude. These results support the existence of a somatosensory mismatch response with features similar to those of the aMMN and highlight the relevance of the somatosensory-specific positivity. Results from the duration experiment also resolve some of the discrepancies between previous studies.

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Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis and subsequent treatment planning. In medical images to visually perceive abnormalities' extent and boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmentation algorithm to segment digital medical image data.Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable dissimilarity between its different regions. The hierarchy represents the continuous merging of similar, spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased.This chapter discusses in detail first the implementation of the HCS process, second the implementation details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and MRI) of a biological sample, third the implementation details of how the process is used as a perception aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpretation aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate.

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