In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification techniques which estimated parameters of the statistical distribution which were based on observation data and ultrasound echo envelope signal. However, in this study, we proposed a new combination of statistical features which were extracted from the ultrasound images and explored their properties for the speckle detection. These features were used as inputs to the unsupervised clustering algorithms for the speckle classification. We used five different types of unsupervised techniques and compared their performance by feeding different combinations of the statistical features. In order to quantitatively compare statistical features and classification methods, as ground truth, we used simulations of cyst and fetus ultrasound images which were generated using Field II ultrasound simulation program[1]. Initial results showed that by combining two statistical models (K and Rayleigh distributions) we can get best speck detection signatures to feed unsupervised classifiers and maximize speckle detection performance.

We've used Near-Infrared Spectroscopy (fNIRS) as a noninvasive tool to monitor blood oxygenation due to the acute pain stimuli. The aim of the study was to find a relationship between the signals recorded by activation of the anterior cingulated cortex (ACC) in healthy subjects, who experience pain via stimulation, and the subject reported pain. These findings will shed light on pain related cognitive studies. Based on our findings, we believe that the fNIRS can be used as a tool for monitoring pain in the brain as well as an effective tool for monitoring the objective efficiency of the pain treatments. Results have shown a correlation between the fNIRS signal and patients' subjective pain level (mild, moderate and severe) which is evidence that the fNIRS is a useful tool for monitoring objective pain response.

In this paper we applied a well-tested neural network, so-called Supervised Fuzzy Adaptive Resonance (SF), to investigate the potential of functional Near-Infra-Red (fNIR) spectroscopy for automated assessment of physical stimulus intensity. To induce mild, moderate and severe physical stimuli, we asked the participants to keep their left hand in the ice water for gradually increased durations. Initial tests with fNIR data from 6 healthy participants (36 trials) indicated that SF is a reliable automated method to estimate the intensity of the induced stimuli with a high accuracy.

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