Siemens Medical Solutions Usa, Inc.

General Electric Company

Ge Medical Systems Global Technology Company, LLC

Tiantian Zhang

Li Zhang

James Zhengshe Liu

Kenneth Scott Kump

Kadri Nizar Jabri

Chuande Liu

Jingyi Liang

Jabri Kadri

A system and method for validating an image segmentation algorithm are provided. The method for validating an image segmentation algorithm comprises: determining a region of interest in an image; segmenting the image from a first point in the region of interest by using a computer-based segmentation algorithm to obtain a first segmentation result; segmenting the image from a second point in the region of interest by using the computer-based segmentation algorithm to obtain a second segmentation result; and comparing the first segmentation result with the second segmentation result to determine a consistency of the computer-based segmentation algorithm.

System and method for validating an image segmentation algorithm

The effects of electromagnetic interference (EMI) on X-ray image data is corrected by characterizing the EMI and processing the image data to subtract the EMI effects from the image data. The X-ray image data, along with offset data, are collected in a conventional manner, affected by EMI if present, and EMI-characterizing data is immediately collected thereafter by disabling rows of a digital detector (FET off). The EMI-characterizing data, then, is not affected by the presence of image data, and can be used to characterize the amplitude and frequency of the EMI. The EMI-characterizing data is assured of being in phase with the collected image and offset data due to its collection in the same image acquisition sequence immediately following the collection of image and offset data. Artifacts due to the presence of EMI are thus eliminated from reconstructed images based upon the corrected data.

Artifact reduction system and method for radiological imaging system

A method and system for ground glass nodule (GGN) segmentation. The invention comprises methods and systems for GGN segmentation using adaptive intensity models in conjunction with Markov random field (MRF) based modeling. The adaptive intensity model overcomes problems associated with low-contrast GGN images. Vessels included by MRF segmentation are removed by shape analysis based on distance maps of the segmentation.

GGN segmentation in pulmonary images for accuracy and consistency

Growing community of inventors

Waukesha, WI, United States of America

Tiantian Zhang