Combination of PCA and undecimated wavelet transform for neural data processing.

Nervous system conveys information by electrical signals called 'spikes', therefore, spikes detection and sorting are challenging topics in the neural data processing. The principal component analysis (PCA) is a convenient tool for clustering spikes; however it has some disadvantages for closely shaped and overlapped spikes. For such the cases, an algorithm based on the combination of the principal component analysis and undecimated wavelet transform, is proposed to enhance the cluster formation from the spikes mapping. These results indicate that the principal component analysis used in combination with the undecimated wavelet has a better performance in the spike sorting. This can lead to more compact and separate clusters in comparison with the PCA clustering and more efficient spike sorting.

Extended Kalman filtering of point process observation.

A temporal point process is a stochastic time series of binary events that occurs in continuous time. In computational neuroscience, the point process is used to model neuronal spiking activity; however, estimating the model parameters from spike train is a challenging problem. The state space point process filtering theory is a new technique for the estimation of the states and parameters. In order to use the stochastic filtering theory for the states of neuronal system with the Gaussian assumption, we apply the extended Kalman filter. In this regard, the extended Kalman filtering equations are derived for the point process observation. We illustrate the new filtering algorithm by estimating the effect of visual stimulus on the spiking activity of object selective neurons from the inferior temporal cortex of macaque monkey. Based on the goodness-offit assessment, the extended Kalman filter provides more accurate state estimate than the conventional methods.

Optimized noninvasive monitoring of thermal changes on digital B-mode renal sonography during revascularization therapy.

OBJECTIVE: Noninvasive real-time thermal change monitoring of human internal organs can play a critical role in diagnosis and treatment of many disorders, including reperfusion of renal arteries during anticoagulation therapy. METHODS: This article focuses on tissue temperature detection using ultrasound velocity changes in different structures and their related speckle shift from their primary locations on high-quality B-mode digital sonography. We evaluated different speckle-tracking techniques and optimized them using appropriate motion estimation methods to determine the best algorithm and parameters. RESULTS: Performing thermal detection methods on simulated phantoms showed a good correlation between speckle shifts and the ground truth temperature. For the simulated images, average thermal error was 0.5 degrees C with an SD of 0.5 degrees C, where lower errors can be obtained in noiseless (motionless) data. The proposed technique was evaluated on real in vivo cases during surgical occlusion and reopening of the renal segmental artery and showed the potential of the algorithm for observation of internal organ changes using only digital ultrasound systems for diagnosis and therapy. CONCLUSIONS: The adaptive Rood pattern search proved to be the best block-matching technique, whereas the multiresolution Horn-Schunck technique was the best gradient optical flow method. The extracted thermal change during in vivo revascularization therapy is promising. In addition, we present an evaluation of several block-matching and optical flow motion estimation techniques.

An efficient temperature estimation using optical-flow in ultrasound B-mode digital images.

A two-dimensional temperature estimation method has been proposed based on the detection of shifts in speckle pattern location of ultrasound B-Mode digital images from a region of tissue undergoing thermal therapy. The speckle pattern shifts were due to the local temperature dependence of sound speed and thermal expansion in the heated region. Here in this paper we made use of optical-flow in order to achieve a better accuracy and lower computational burden. The accuracy of measurement of the temperature has been tested on simulated images and it is experimentally validated using tissue mimicking phantom. Good agreement was obtained from the simulated sequences (mean difference=0.1 and pick error was 0.6). The proposed method was found computationally faster compared to our previous work which was based on the cross correlation algorithm.

A human auditory tuning curves matched wavelet function.

This paper proposes a new quantitative approach to the problem of matching a wavelet function to a human auditory tuning curves. The auditory filter shapes were derived from the psychophysical measurements in normal-hearing listeners using the variant of the notched-noise method for brief signals in forward and simultaneous masking. These filters were used as templates for the designing a wavelet function that has the maximum matching to a tuning curve. The scaling function was calculated from the matched wavelet function and by using these functions, low pass and high pass filters were derived for the implementation of a filter bank. Therefore, new wavelet families were derived.

Noninvasive temperature estimation using sonographic digital images.

OBJECTIVE: The purpose of this study was to develop and evaluate a speckle-tracking method for tissue temperature estimation due to heating fields using digital sonographic images. METHODS: The temperature change estimation method is based on the thermal dependence of the ultrasound speed and the thermal expansion of the medium. Local changes in the speed of sound due to changes in the temperature produce apparent displacement of the scatterers, and the expansion introduces physical displacement. In our study, a new technique has been introduced in which the axial physical displacements were obtained from digital sonographic images. The axial speckle pattern displacement was determined with a cross-correlation algorithm. The displacement data were then used for computing the temperature changes. To monitor the temperature in real time, the computational time was decreased by restricting the search region in the cross-correlation algorithm and carrying out the cross-correlation function in the frequency domain via a fast Fourier transform algorithm. RESULTS: Experiments were performed on tissue-mimicking phantoms. The imaging probe was a commercial linear array working at 10 MHz. In addition, the temperature changes during heating were measured invasively by negative temperature coefficient thermistors. There was good agreement between ultrasonic temperature estimations and invasive temperature measurements. CONCLUSIONS: The proposed method verifies the capability of the speckle-tracking algorithm for determining both the magnitude and direction of displacement. The average error was 0.2 degrees C; the maximum error was 0.53 degrees C; and the SD was 0.19 degrees C. Therefore, the proposed algorithm is capable of extracting the temperature information from sonographic digital images.