Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ll as the accuracy of the method re 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 re much larger than the actual volumes. Clinical experiments re conducted, and the fuzzy logic method gave a volume lor than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility.
['Stéphane Vial', 'D. Gibon', 'Christian Vasseur', 'Jean Rousseau']
I am writing a research paper on a idea Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images
[{"from": "human", "value": "I am writing a research paper on a idea Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ll as the accuracy of the method re 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 re much larger than the actual volumes. Clinical experiments re conducted, and the fuzzy logic method gave a volume lor than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility."}]