Fuzzy model-based body-wide anatomy recognition in medical images

Abstract

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) becomes essential. Previously, we presented a fuzzy object modeling strategy for AAR. This paper presents several advances in this project including streamlined definition of open-ended anatomic objects, extension to multiple imaging modalities, and demonstration of the same AAR approach on multiple body regions. The AAR approach consists of the following steps: (a) Collecting image data for each population group G and body region B. (b) Delineating in these images the objects in B to be modeled. (c) Building Fuzzy Object Models (FOMs) for B. (d) Recognizing individual objects in a given image of B by using the models. (e) Delineating the recognized objects. (f) Implementing the computationally intensive steps in a graphics processing unit (GPU). Image data are collected for B and G from our existing patient image database. Fuzzy models for the individual objects are built and assembled into a model of B as per a chosen hierarchy of the objects in B. A global recognition strategy is used to determine the pose of the objects within a given image I following the hierarchy. The recognized pose is utilized to delineate the objects, also hierarchically. Based on three body regions tested utilizing both computed tomography (CT) and magnetic resonance (MR) imagery, recognition accuracy for non-sparse objects has been found to be generally sufficient ( 3 to 11 mm or 2-3 voxels) to yield delineation false positive (FP) and true positive (TP) values of < 5% and ≥ 90%, respectively. The sparse objects require further work to improve their recognition accuracy.