Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images

• Published in: IEEE transactions on medical imaging Vol. 20; no. 12; pp. 1362 - 1372
• Main Authors: Vial, S., Gibon, D., Vasseur, C., Rousseau, J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active shape model; Artificial Intelligence; Biological and medical sciences; Biomedical imaging; Brain Neoplasms - diagnosis; Brain Neoplasms - secondary; Computer Simulation; Computerized tomography; Computerized, statistical medical data processing and models in biomedicine; Contrast Sensitivity; Fuzzy Logic; Fuzzy sets; General aspects. Methods; Humans; Image segmentation; Imaging phantoms; Imaging, Three-Dimensional - methods; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Medical sciences; Meningeal Neoplasms - diagnosis; Meningeal Neoplasms - secondary; Meningioma - diagnosis; Meningioma - secondary; Miscellaneous. Technology; Models, Neurological; Nonlinear Dynamics; Pathology; Phantoms, Imaging; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Reproducibility of Results; Sensitivity and Specificity; Software Validation; Studies; Testing; Three dimensional; Tomography; Two dimensional displays; Human; 3D imaging; Radiodiagnosis; Segmentation; Image processing; Central nervous system; Nuclear magnetic resonance imaging; Optimization; Fuzzy logic; Biomedical data processing; Image analysis; Fuzzy algorithm; Volume; Tomography; Edge detection; Biomedical engineering; Brain (vertebrata); Test object; Signal to noise ratio
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.974931

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 /spl mu/=1), and a maximum volume outside which no part of the volume is expected to be found (membership degree /spl mu/=0), are defined semi-automatically. The intermediate fuzziness region (0</spl 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 cm/sup 3/, 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.