Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Mott-Hubbard quantum criticality in paramagnetic CMR pyrochlores

We present a correlated {\it ab initio} description of the paramagnetic phase of Tl$_2$Mn$_2$O$_7$, employing a combined local density approximation (LDA) with multiorbital dynamical mean field theory (DMFT) treatment. We show that the insulating state observed in this colossal magnetoresistance (CMR) pyrochlore is determined by strong Mn intra- and inter-orbital local electron-electron interactions. Hybridization effects are reinforced by the correlation-induced spectral weight transfer. Our result coincides with optical conductivity measurements, whose low energy features are remarkably accounted for by our theory. Based on this agreement, we study the disorder-driven insulator-metal transition of doped compounds, showing the proximity of Tl$_2$Mn$_2$O$_7$ to quantum phase transitions, in agreement with recent measurements.Comment: 4 pages, 4 figure

Spontaneous creation of macroscopic flow and metachronal waves in an array of cilia

Cells or bacteria carrying cilia on their surface show many striking features : alignment of cilia in an array, two-phase asymmetric beating for each cilium, coordination between cilia and existence of metachronal waves with a constant phase difference between two adjacent cilia. We give simple theoretical arguments based on hydrodynamic coupling and an internal mechanism of the cilium derived from the behavior of a collection of molecular motors, to account qualitatively for these cooperative features. Hydrodynamic interactions can lead to the alignment of an array of cilia. We study the effect of a transverse external flow and obtain a two-phase asymmetrical beating, faster along the flow and slower against the flow, proceeding around an average curved position. We show that an aligned array of cilia is able to spontaneously break the left-right symmetry and to create a global average flow. Metachronism arises as a local minimum of the beating threshold and leads to a rather constant flow

Human detection on omnidirectional camera imagery by multi-feature fusion based on gradients, color and local phase information

Field of view of the traditional camera is limited such that usually more than three cameras are needed to cover the entire surveillance area. The use of multiple cameras usually require more efforts regarding camera control and set up as well as they need additional algorithms to find the relationships among the images of different cameras. In this research work, we present a multi-feature algorithm that employs only one omnidirectional camera instead of using multiple cameras to cover the entire surveillance region. Here we use the image gradients, the local phase information based on phase congruency, the phase congruency magnitude, and the color features. These features are fused together to build one descriptor named as “Fused Phase, Gradients and Color features (FPGC). The image gradients, and local phase information based on phase congruency concept are used to extract the human body shape features. Either LUV or grayscale channel features are used according to the kind of camera used. The phase congruency magnitude and orientation of each pixel in the input image is computed with respect to its neighborhood. The resultant images are divided into local regions and the histogram of oriented phase, and the histogram of oriented gradients are determined for each local region and combined. A large pool of the candidate features is randomly generated for one channel of the phase congruency magnitude and three LUV color channels. All these features are fed to a decision tree Adaboost classifier for training and classification between the classes. The proposed approach is evaluated on a challenging omnidirectional dataset and observed promising performance.https://ecommons.udayton.edu/stander_posters/2282/thumbnail.jp