title: Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.
abstract: OBJECTIVE
  Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential.
  
  
  METHODS
  This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor.
  
  
  RESULTS
  Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features.
  
  
  CONCLUSIONS
  This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems.
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: abdolali.fatemeh@gmail.com.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Abdolali
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
authors.forename: Reza Aghaeizadeh
authors.initials: RA
authors.lastname: Zoroofi
authors.affiliation: Graduate school of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Otake
authors.affiliation: Graduate school of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Sato
mesh_terms: D001331:Automation; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003560:Cysts / Q000000981:diagnostic imaging*; D005145:Face / Q000473:pathology*; D006801:Humans; D008437:Maxilla / Q000473:pathology*
keywords: Computer aided detection; Cone beam CT; Contourlet; Maxillofacial cyst; Spherical Harmonics