title: Selection of MR images for automated segmentation.
abstract: MR images show a large range of contrast for various tissues in the body and are ideal for multispectral segmentation. Typically, only two MR images (dual-echo series) are used for segmentation; however, other images are often available. We evaluated MR images from 40 patients to determine the optimal type and number of images required for segmentation of tissues associated with brain tumors (normal brain, edema, necrosis, and active tumor). Pattern recognition methods indicated that three MR images from the same slice location were adequate for segmentation, as defined by feature selection and feature extraction measures based on training fields. This result was also confirmed by visually examining segmented images for all 40 patients. This work demonstrates that by using existing image/statistical analysis techniques (feature selection and feature extraction), one can systematically determine the optimal type and number of MR images for tissue segmentation.
authors.affiliation: University of Texas M.D. Anderson Cancer Center, Department of Diagnostic Radiology, Houston 77030, USA.
authors.forename: K J
authors.initials: KJ
authors.lastname: McClain
authors.forename: Y
authors.initials: Y
authors.lastname: Zhu
authors.forename: J D
authors.initials: JD
authors.lastname: Hazle
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis*; D017352:Echo-Planar Imaging / Q000295:instrumentation / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods; D012680:Sensitivity and Specificity