BioIngenium Group
Centro de Telemedicina
Universidad Nacional de Colombia
 
     
  Distributed Genetic Algorithm for Subtraction Radiography  
     
  5. Conclusions  
 
Digital subtraction radiography detects tissue mass changes by subtracting two digital radiographs. This method has shown to be very useful in early diagnosis of disease and follow-up examination. When subtracting two radiographs taken over time, the image features which are coincident to both images can be removed and the small changes can be amplified to highlight their presence.

For many years, digital subtraction radiography in dentistry has been used to qualitatively assess changes in radiographic density. Numerous authors have demonstrated the ability of this method to improve diagnostic performance for the detection of approximal dental caries, periapical pathology and periodontal disease. The use of digital subtraction radiography has also been shown to markedly increase the detection of destruction in the periodontal bone, as well as secondary caries detection.

A large variety of odontological diseases result in destruction of mineralized tissues, which are relatively small in the initial progression of the disease. A reliable detection and follow-up examination necessarily requires a precise alignment of the two images for the tissue changes to be detectable. Different approaches have been proposed for correcting such geometrical distortions. It goes from manual correction to different devices used to ensure a consistent geometric projection with can be reliably reproduced over time.

In this research, an entirely automatic method is proposed for spatial radiographic alignment. The process starts by selecting either of the two images as the reference while the other is considered the floating image. Afterward illumination differences are eliminated by means of an equalization algorithm explained below. Then consecutive affine transformations are performed on the floating image and the transformed image is compared to the reference using the correlation ratio as the similarity measure. An adaptive GA is used in order to find the transformation that produces the best match. The process is robust, reliable and reproducible on the test group of images.
 
     
     
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