Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

Yubing Tong, J. K. Udupa, D. Odhner, Sanghun Sin, R. Arens

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

4 Scopus citations


Obstructive Sleep Apnea Syndrome (OSAS) is common in obese children with risk being 4.5 fold compared to normal control subjects. Polycystic Ovary Syndrome (PCOS) has recently been shown to be associated with OSAS that may further lead to significant cardiovascular and neuro-cognitive deficits. We are investigating image-based biomarkers to understand the architectural and dynamic changes in the upper airway and the surrounding hard and soft tissue structures via MRI in obese teenage children to study OSAS. At the previous SPIE conferences, we presented methods underlying Fuzzy Object Models (FOMs) for Automatic Anatomy Recognition (AAR) based on CT images of the thorax and the abdomen. The purpose of this paper is to demonstrate that the AAR approach is applicable to a different body region and image modality combination, namely in the study of upper airway structures via MRI. FOMs were built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. FOMs encode the uncertainty and variability present in the form and relationships among the objects over a study population. Totally 11 basic objects (17 including composite) were modeled. Automatic recognition for the best pose of FOMs in a given image was implemented by using four methods - a one-shot method that does not require search, another three searching methods that include Fisher Linear Discriminate (FLD), a b-scale energy optimization strategy, and optimum threshold recognition method. In all, 30 multi-fold cross validation experiments based on 15 patient MRI data sets were carried out to assess the accuracy of recognition. The results indicate that the objects can be recognized with an average location error of less than 5 mm or 2-3 voxels. Then the iterative relative fuzzy connectedness (IRFC) algorithm was adopted for delineation of the target organs based on the recognized results. The delineation results showed an overall FP and TP volume fraction of 0.02 and 0.93.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationComputer-Aided Diagnosis
StatePublished - 2013
EventMedical Imaging 2013: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: Feb 12 2013Feb 14 2013

Publication series

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


OtherMedical Imaging 2013: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL


  • Fuzzy connectedness
  • Fuzzy models
  • Object recognition
  • Obstructive sleep apnea
  • Segmentation

ASJC Scopus subject areas

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


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