Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system

  • Authors:

  • Jung Leng Foo;Go Miyano;Thom Lobe;Eliot Winer

  • Affiliations:

  • Virtual Reality Applications Center, 1620 Howe Hall, Iowa State University, Ames, IA 50011, USA;Pediatric General and Urogenital Surgery Department, Juntendo University School of Medicine, Tokyo, Japan;Pediatric Surgery Department, Blank's Children Hospital, Des Moines, IA, USA;Mechanical Engineering and Human-Computer Interaction Department, Iowa State University, Ames, IA, USA

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2009

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Abstract

A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI's spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. With a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Test cases success in segmenting the tumor from seven of the 10 CT datasets with