VIEWPOINT RECOGNITION IN COMPUTER TOMOGRAPHY IMAGES

摘要

A solution is presented for cardiac CT viewpoint recognition to identify the desired images for a specific view and subsequent processing and anatomy recognition. A new set of features is presented to describe the global binary pattern of cardiac CT images characterized by the highly attenuating components of the anatomy in the image. Five classic image texture and edge feature sets are used to devise a classification approach based on SVM classification, class likelihood estimation, and majority voting, to classify 2D cardiac CT images into one of six viewpoint categories that include axial, sagittal, coronal, two chamber, four chamber, and short axis views. Such an approach results in an accuracy of 99.4% in correct labeling of the viewpoints.

主权项

1. A system for computed tomography (CT) viewpoint recognition in CT images comprising:
a feature engine comprising a global binary pattern feature calculation unit and at least one image feature calculation unit, said global binary pattern feature calculation unit: (i) preprocessing said CT images with histogram equalization to maximize contrast in one or more pre-determined regions, (ii) extracting connected components from said preprocessed CT images, and (iii) forming a feature vector based on said extracted connected components; a cognitive engine comprising: (i) a plurality of classifiers, one for each of said global binary calculation unit, and (ii) at least one image feature calculation unit; and a voting unit picking a class label for each of the CT images based on a majority voting scheme determining the most frequently returned label among said classifiers.