Riverside Research Institute

New York University

Columbia University

Jonathan Mamou

Jeffrey A Ketterling

Yao Rong Wang

Daniel H Turnbull

Jen-wei Kuo

Orlando Aristizabal

Ernest Joseph Feleppa

Ronald H Silverman

Xuan Zhao

Jerry Sebag

Daniel Rohrbach

A method to characterize shape variations in brain ventricles during embryonic growth in mammals, the method including extracting a brain ventricle skeleton from one or more images, calculating a volume profile for the skeleton using the extracted images, partitioning the brain ventricle based on the volume profile along the skeleton, the brain ventricle being partitioned into two lateral ventricles and a main ventricle, the main ventricle being further partitioned into three sub regions, determining volume vectors of the two lateral ventricles and the three sub regions, computing a means square error between the determined computed volume vectors and a pretrained mean volume vector of embryos during different gestational stages, and classifying the embryo to the gestational stage having the lowest mean square error. A method to characterize mutant detection in mammals, the method including acquiring one or more images, computing a volume profile directly along a path of the detected skeleton from the one or more images, aligning the volume profile against a standard profile, and evaluating the volume profile against the standard profile to detect a mutation.

Method for gestational age estimation and embryonic mutant detection

An ultrasonic material-evaluation or classification method using spectral and envelope-statistics variables from backscattered ultrasound echo signals using an adaptive-bandwidth and combined with global variables. This classification method can be applied to any organ or tissue among biological materials and any non-biological material that produces backscattered signals as a result of microscopic internal in homogeneities such as a crystalline structure.

Typing and imaging of biological and non-biological materials using quantitative ultrasound

A method to segment images that contain multiple objects in a nested structure including acquiring an image; defining the multiple objects by layers, each layer corresponding to one region, where a region contains an innermost object and all the objects nested within the innermost object; stacking the layers in an order of the nested structure of the multiple objects, the stack of layers having at least a top layer and a bottom layer; extending each layer with padded nodes; connecting the top layer to a sink and the bottom layer to a source, wherein each intermediate layer between the top layer and the bottom layer are connected only to the adjacent layer by undirected links; and measuring a boundary length for each layer.

Method for automatic tissue segmentation of medical images

A quantitative method to analyze phase-resolved, raw backscatter echo data or the envelope of phase-resolved, raw backscatter echo data to characterize vitreous inhomogeneities as they relate to normal aging and vitreo-retinal disease. The technique can be applied to 2D or 3D data acquired from the vitreous. The approach provides an objective end value to characterize the vitreous and provide a tool for early diagnosis, monitoring and planning treatment of vitreo-retinal diseases.

Methods for diagnosing vitreo-retinal disease

The present invention provides a method for detecting and locating brachytherapy seeds using Singular Spectrum Analysis (SSA). The method exploits the presence of seed-specific repetitive echo signals in detecting seeds and determining their location. Radio frequency (rf) echo signals received from a scanned area of interest are converted to zero-mean envelope-detected signals and then processed using SSA to produce a spectral-power line (a P line). The P line indicates the relative likelihood of a seed being present. A colored P-mode image derived from a set of P lines is superimposed on a conventional ultrasound B-mode image to depict seed location.

Growing community of inventors

New York, NY, United States of America

Jonathan Mamou