Riverain Medical Group, LLC

Riverain Technologies LLC

Steve W Worrell

Jason F Knapp

Praveen Kakumanu

Aarti Raheja

Image processing techniques may include a methodology for normalizing medical image and/or voxel data captured under different acquisition protocols and a methodology for suppressing selected anatomical structures from medical image and/or voxel data, which may result in improved detection and/or improved rendering of other anatomical structures. The technology presented here may be used, e.g., for improved nodule detection within computed tomography (CT) scans. While presented here in the context of nodules within the lungs, these techniques may be applicable in other contexts with little modification, for example, the detection of masses and/or microcalcifications in full field mammography or breast tomosynthesis based on the suppression of glandular structures, parenchymal and vascular structures in the breast.

Suppression of vascular structures in images

Image analysis techniques applicable to mammograms and other types of images may include image normalization, image segmentation, forming a prediction bias image, and creating an equalized image based on the prediction bias image. Creation of the equalized image may include subtraction of the prediction bias image from the original image. Forming the prediction bias image may involve the use of trained predictors.

Machine learnng techniques for pectoral muscle equalization and segmentation in digital mammograms

An image may be processed to normalize and/or remove noise from the image. The processing of the image may involve decomposition of the image into multiple components and subsequent gray scale registration across multiple scales.

Multi-scale image normalization and enhancement

A method of obtaining one or more components from an image may include normalizing and pre-processing the image to obtain a processed image. Features may be extracted from the processed image. Neural-network-based regression may then be performed on the set of extracted features to predict the one or more components. These techniques may be applied, for example, to the problem of extracting and removing bone components from radiographic images, which may be thoracic (lung) images.

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Springboro, OH, United States of America

Steve W Worrell