Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images.
Techniques of three-dimensional (3-D) volume delineation from tomographic medical imaging are usually based on 2-D contour definition. For a given structure, several different contours can be obtained depending on the segmentation method used or the user's choice. The goal of this work is to develop a new method that reduces the inaccuracies generally observed. A minimum volume that is certain to be included in the volume concerned (membership degree mu = 1), and a maximum volume outside which no part of the volume is expected to be found (membership degree mu = 0), are defined semi-automatically. The intermediate fuzziness region (0 < mu < 1) is processed using the theory of possibility. The resulting fuzzy volume is obtained after data fusion from multiplanar slices. The influence of the contrast-to-noise ratio was tested on simulated images. The influence of slice thickness as well as the accuracy of the method were studied on phantoms. The absolute volume error was less than 2% for phantom volumes of 2-8 cm3, whereas the values obtained with conventional methods were much larger than the actual volumes. Clinical experiments were conducted, and the fuzzy logic method gave a volume lower than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility.
Vial
S
S
Laboratoire de Biophysique (UPRES EA 1049), ITM, Hôpital Universitaire, and Université des Sciences et Technologies, Lille, France.
Gibon
D
D
Vasseur
C
C
Rousseau
J
J
D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000556:secondary; D003198:Computer Simulation; D015350:Contrast Sensitivity; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008577:Meningeal Neoplasms / Q000175:diagnosis* / Q000556:secondary; D008579:Meningioma / Q000175:diagnosis* / Q000556:secondary; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012986:Software Validation