title: Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.
abstract: BACKGROUND
  Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.
  
  
  METHODS
  In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures.
  
  
  RESULTS
  Experimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets.
  
  
  CONCLUSIONS
  The proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.
authors.forename: Aouache
authors.initials: A
authors.lastname: Mustapha
authors.affiliation: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Univeristi Kebangsaan Malaysia, Bangi, 43600 Selangor DE, Malaysia. aini@eng.ukm.my.
authors.forename: Aini
authors.initials: A
authors.lastname: Hussain
authors.forename: Salina Abdul
authors.initials: SA
authors.lastname: Samad
authors.forename: Mohd Asyraf
authors.initials: MA
authors.lastname: Zulkifley
authors.forename: Wan Mimi
authors.initials: WM
authors.lastname: Diyana Wan Zaki
authors.forename: Hamzaini Abdul
authors.initials: HA
authors.lastname: Hamid
mesh_terms: D000465:Algorithms; D002574:Cervical Vertebrae / Q000000981:diagnostic imaging; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging; D016571:Neural Networks, Computer; D013122:Spinal Diseases / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed