title: Automatic recognition of anatomical regions in three-dimensional medical images.
abstract: This paper presents a method that detects anatomy regions in three-dimensional medical images. The method labels each axial slice of the image according to the anatomy region it belongs to. The detected regions are the head (and neck), the chest, the abdomen, the pelvis, and the legs. The proposed method consists of two main parts. The core of the algorithm is based on a two-dimensional feature extraction that is followed by a random forest classification. This recognition process achieves an overall accuracy of 91.5% in slice classification, but it cannot always provide fully consistent labeling. The subsequent post-processing step incorporates the expected sequence and size of the human anatomy regions in order to improve the accuracy of the labeling. In this part of the algorithm the detected anatomy regions (represented by Gaussian distributions) are fitted to the region probabilities provided by the random forest classifier. The proposed method was evaluated on a set of whole-body MR images. The results demonstrate that the accuracy of the labeling can be increased to 94.1% using the presented post-processing. In order to demonstrate the robustness of the proposed method it was applied to partial MRI scans of different sizes (cut from the whole-body examinations). According to the results the proposed method works reliably (91.3%) for partial body scans (having as little length as 35cm) as well.
authors.affiliation: Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Magyar tudósok körútja 2, H-1119 Budapest, Hungary. Electronic address: tmarton@iit.bme.hu.
authors.forename: Márton
authors.initials: M
authors.lastname: Tóth
authors.affiliation: GE Hungary Healthcare Division, Petőfi Sándor sgt 10, 6722 Szeged, Hungary.
authors.forename: László
authors.initials: L
authors.lastname: Ruskó
authors.affiliation: Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Magyar tudósok körútja 2, H-1119 Budapest, Hungary.
authors.forename: Balázs
authors.initials: B
authors.lastname: Csébfalvi
mesh_terms: D000005:Abdomen / Q000000981:diagnostic imaging; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003663:Decision Trees; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007866:Leg / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D013909:Thorax / Q000000981:diagnostic imaging; D055815:Young Adult
keywords: Anatomy detection; Content-based medical image processing; Expectation maximization; Random-forest classification