Neural tree network method for image segmentation

Sumitro Samaddar, Richard J. Mammone

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present an extension of the neural tree network (NTN) architecture to let it solve multi-class classification problems with only binary fan-out. We then demonstrate it's effectiveness by applying it in a method for image segmentation. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsJames L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C.A. Thomopoulos, al et al
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Number of pages12
ISBN (Print)0819413666
StatePublished - 1994
Externally publishedYes
EventSubstance Identification Analytics - Innsbruck, Austria
Duration: Oct 4 1993Oct 8 1993

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherSubstance Identification Analytics
CityInnsbruck, Austria

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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