Arch Development Corporation

University of Chicago

Hiroyuki Yoshida

Kunio Doi

Robert M Nishikawa

Takehiro Ema

Wei Zhang

Shigehiko Katsuragawa

Li Lan

Yoshitaka Masutani

Abraham Dachman

Yali Amit

Janne Nappi

Peter Maceneaney

David Rubin

A method, system, and computer program product for identifying at least one three-dimensionally extended lesion within a volumetric region encompassing an inner surface, an outer surface, and intervening tissue of a target organ. The method includes: (1) generating a set of voxels representing a total scanned volume from a set of cross-sectional images of the target organ; (2) performing segmentation to extract a set of voxels representing the volumetric region from the set of voxels representing the total scanned volume; (3) detecting a set of candidate lesions based on geometric feature values of each voxel in the set of voxels representing the volumetric region; and (4) selecting the at least one three-dimensionally extended lesion from the set of candidate lesions based on at least one of volumetric, morphologic, and texture feature values of each lesion in the set of candidate lesions.

Method for computer-aided detection of three-dimensional lesions

A method and apparatus for discrimination of nodules and false positives in digital chest radiographs, using a wavelet snake technique. The wavelet snake is a deformable contour designed to identify the boundary of a relatively round object. The shape of the snake is determined by a set of wavelet coefficients in a certain range of scales. Portions of the boundary of a nodule are first extracted using a multiscale edge representation. The multiscale edges are then fitted by a gradient descent procedure which deforms the shape of a wavelet snake by changing its wavelet coefficients. The degree of overlap between the fitted snake and the multiscale edges is calculated and used as a fit quality indicator for discrimination of nodules and false detections.

Method, apparatus, and storage medium for detection of nodules in

A computer-aided diagnosis (CAD) method for detection of clustered microcalcifications in digital mammograms based on an image reconstruction using a substantially optimally weighted wavelet transform. Weights at individual scales of the wavelet transform are optimized based on a supervised learning method. In the learning method, an error function represents a difference between a desired output and a reconstructed image obtained from weighted wavelet coefficients of the wavelet transform for a given mammogram. The error function is then minimized by modifying the weights by means of a conjugate gradient algorithm. Performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis which indicated that the present invention outperformed both a difference-image technique and partial reconstruction method currently used in CAD methods.

Method for determining an optimally weighted wavelet transform based on

A method for automated detection of abnormal anatomic regions, wherein a mammogram is digitized to produce a digital image and the digital image is processed using local edge gradient analysis and linear pattern analysis in addition to feature extraction routines to identify abnormal anatomic regions. Noise reduction filtering and pit-filling/spike-removal filtering techniques are also provided. Multiple difference imaging techniques are also used in which difference images employing different filter characteristics are obtained and processing results logically OR'ed to identify abnormal anatomic regions. In another embodiment the processing results with and without noise reduction filtering are logically AND'ed to improve detection sensitivity. Also, in another embodiment the wavelet transform is utilized in the identification and detection of abnormal regions. The wavelet transform is preferably used in conjunction with the difference imaging technique with the results of the two techniques being logically OR'ed.

Computer-aided method for image feature analysis and diagnosis in

Growing community of inventors

Westmont, IL, United States of America

Hiroyuki Yoshida