University of Pennsylvania

Dewey Odhner

Jayaram K Udupa

Drew A Torigian

Yubing Tong

Andre Souza

George Grevera

Tiange Liu

A computerized method of providing automatic anatomy recognition (AAR) includes gathering image data from patient image sets, formulating precise definitions of each body region and organ and delineating them following the definitions, building hierarchical fuzzy anatomy models of organs for each body region, recognizing and locating organs in given images by employing the hierarchical models, and delineating the organs following the hierarchy. The method may be applied, for example, to body regions including the thorax, abdomen and neck regions to identify organs.

Applications of automatic anatomy recognition in medical tomographic imagery based on fuzzy anatomy models

Quantification of body composition plays an important role in many clinical and research applications. Radiologic imaging techniques such as Dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), and computed tomography (CT) imaging make accurate quantification of the body composition possible. This disclosure presents an automated, efficient, accurate, and practical body composition quantification method for low dose CT images; method for quantification of disease from images; and methods for implementing virtual landmarks.

Quantification and staging of body-wide tissue composition and of abnormal states on medical images via automatic anatomy recognition

Interactive non-uniformity correction (NC) and interactive intensity standardization (IS) require sample tissue regions to be specified for several different types of tissues. Interactive NC estimates the degree of non-uniformity at each voxel in a given image, builds a global function for non-uniformity correction, and then corrects the image to improve quality. Interactive IS includes two steps: a calibration step and a transformation step. In the first step, tissue intensity signatures of each tissue from a few subjects are utilized to set up key landmarks in a standardized intensity space. In the second step, a piecewise linear intensity mapping function is built between the same tissue signatures derived from the given image and those in the standardized intensity space to transform the intensity of the given image into standardized intensity. Interactive IS for MR images combined with interactive NC can substantially improve numeric characterization of tissues.

Interactive non-uniformity correction and intensity standardization of MR images

Methods for the improved interactive segmentation of medical image slice data using a computer include the novel combination of the well-known live wire and snakes methods. The improved techniques automatically insert new anchor points for each medical image slice that is processed. The improved methods called iterative live wire and live snakes result in a segmentation process that is faster, more accurate, and requires less operator interaction than the previous methods while still allowing an operator to make adjustments to the segmentation as the process moves from one image slice to the next.

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Horsham, PA, United States of America

Dewey Odhner