Segmentation of radiographic cervical images with neuro-fuzzy classification of multiresolution wavelets

Surya Pemmaraju, Sunanda Mitra, Yao Yang Shieh, Glenn Roberson

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Segmentation of medical images poses a critical problem in image analysis. Segmenting a scene into different regions in the absence of sufficient apriori information is a challenging problem. A multiresolution image representation approach is presented here which makes use of a fuzzy neural network to segment a reconstructed image from wavelet decomposition into regions of interest. The multiresolution wavelets provide a basis for analyzing the information content of the image with global as well as local perspectives. The higher resolution levels contain information pertaining to the finer details while the lower resolutions capture the global features. A neuro-fuzzy algorithm facilitates the segmentation of the wavelet reconstructed image into different regions based on image intensity. The proposed algorithm has been applied to images of different kinds and has yielded promising results. The concept of using multiresolution wavelets and a neuro-fuzzy classification scheme has the added advantage of flexibility in the level of segmentation achieved.

Original languageEnglish
Pages (from-to)216-224
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - May 12 1995
EventMedical Imaging 1995: Image Processing - San Diego, United States
Duration: Feb 26 1995Mar 2 1995


  • Neuro-fuzzy clustering
  • Radiographic images
  • Segmentation
  • Wavelets


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