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Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy
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Automatic cephalometric analysis /certified fixed orthodontic courses by Indian dental academy

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The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.

Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078

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  • 1. INDIAN DENTAL ACADEMY Leader in continuing dental education www.indiandentalacademy.com www.indiandentalacademy.com
  • 2.  literature review focused on a single question which tries to identify, appraise, select and synthesize all high quality research evidence relevant to that question www.indiandentalacademy.com
  • 3.  Highest level of medical evidence  An understanding of systematic reviews and how to implement them in practice is becoming mandatory for all professionals involved in the delivery of health care. www.indiandentalacademy.com
  • 4.    Broadbent & Hofrath – cephalometer 1931 contributed to the analysis of malocclusion standardized diagnostic method in orthodontic practice and research www.indiandentalacademy.com
  • 5.  Two approaches  a manual approach  a computer- aided approach www.indiandentalacademy.com
  • 6.  uses manual identification of landmarks,  based either on an overlay tracing of the radiograph to identify anatomical or  constructed points followed by the transfer of the tracing to a digitizer linked to a computer, or  a direct digitization of the lateral skull radiograph using a digitizer linked to a computer and then locating landmarks on the monitor.  the computer software completes the cephalometric analysis automatically www.indiandentalacademy.com
  • 7. Landmarks digitized directly from patient. – the DIGIGRAPH JCO Volume 1990 Jun(360 - 367): The DigiGraph Work Station Part 1 Basic Concepts - SPIRO J. CHACONAS, DDS, MS; GARY A. ENGEL, AB, MS; ANTHONY A. GIANELLY, DM www.indiandentalacademy.com
  • 8.  Cohen 1984  a scanned or digital cephalometric radiograph is stored in the computer and loaded by the software.  The software then automatically locates the landmarks and performs the measurements for cephalometric analysis www.indiandentalacademy.com
  • 9.  landmark detection  calculations have already been automated with success www.indiandentalacademy.com
  • 10.  Image filtering plus knowledge-based landmark search  Model- based approaches  Soft-computing approaches  Hybrid approaches www.indiandentalacademy.com
  • 11.  Resolution pyramid  Edge enhancement  Knowledge-based extraction  Gray level value difference www.indiandentalacademy.com
  • 12. Resolution pyramid Pyramid or 'pyramid representation' is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling  Historically, pyramid representation is a predecessor to scale space representation and multiresolution analysis  www.indiandentalacademy.com
  • 13. Edge detection  in image processing and computer vision, particularly in within the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuites www.indiandentalacademy.com
  • 14.  In computing, a grayscale or greyscale digital image is an image in which the value of each pixel is a single sample, that is, it carries the full (and only) information about its intensity. Images of this sort are composed exclusively of shades of neutral gray, varying from black at the weakest intensity to white at the strongest. www.indiandentalacademy.com
  • 15. Advantages  Easy to implement  Image filtering techniques are well studied and a large number are available  By encoding proper anatomical knowledge better accuracy is achievable www.indiandentalacademy.com
  • 16. Disadvantages Can fail to capture morphological variability in the radiographs  Filtering results are highly dependent on image quality and intensity level  Sensitive to noise in the image  Not all landmarks lie on edge and, moreover, the edges or curves are often unclear  www.indiandentalacademy.com
  • 17.  Pattern matching  Spatial spectroscopy  Active shape models  Active contours with similarity function  Active appearance model www.indiandentalacademy.com
  • 18. Pattern matching  In computer science, pattern matching is the act of checking for the presence of the constituents of a given pattern. In contrast to pattern recognition, the pattern is rigidly specified. Such a pattern concerns conventionally either sequences or tree structures. Pattern matching is used to test whether things have a desired structure, to find relevant structure, to retrieve the aligning parts, and to substitute the matching part with something else www.indiandentalacademy.com
  • 19. Active Shape Models (ASMs)  are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image. The shapes are constrained by the PDM (Point Distribution Model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model) www.indiandentalacademy.com
  • 20.  segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.[1] Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. www.indiandentalacademy.com
  • 21. Active Appearance Model (AAM) Computer Vision algorithm for matching a statistical model of object shape and appearance to a new image. They are built during a training phase. A set of images together with coordinates of landmarks, that appear in all of the images is provided by the training supervisor.  The approach is widely used for matching and tracking faces and for Medical Image Interpretation.  www.indiandentalacademy.com
  • 22. Advantages Is invariant to scale, rotation, and translation (the structure can be located even if it is smaller or bigger than the given model)  Accommodates shape variability  www.indiandentalacademy.com
  • 23. Disadvantages     Needs models that must be created by averaging the variations in shape of each anatomical structure in a given set of radiographs Model deformation must be constrained and is not always precise Cannot be applied to partially hidden regions Sensitive to noise in the image www.indiandentalacademy.com
  • 24.  PCNN (pulse coupled neural networks)  Support vector machines  Genetic algorithms  Fuzzy neural networks www.indiandentalacademy.com
  • 25. Pulse-coupled networks or Pulse-Coupled Neural Networks (PCNNs) are neural models proposed by modeling a cat’s visual cortex and developed for highperformance biomimetic image processing.  Over the past decade, PCNNs have been utilized for a variety of image processing applications, including: image segmentation, feature generation, face extraction, motion detection, region growing, noise reduction, and so on  www.indiandentalacademy.com
  • 26.  'Support vector machines (SVMs)' are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. They can also be considered a special case of Tikhonov regularization. A special property of SVMs is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers. www.indiandentalacademy.com
  • 27. genetic algorithm (GA) Search technique used in computing to find exact or approximate solutions to optimization and search problems.  Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).  www.indiandentalacademy.com
  • 28.  A neuro-fuzzy network is a fuzzy inference system in the body of an artificial neural network. Depending on the FIS type, there are several layers that simulate the processes involved in a fuzzy inference like fuzzification, inference, aggregation and defuzzification. Embedding an FIS in a general structure of an ANN has the benefit of using available ANN training methods to find the parameters of a fuzzy system. www.indiandentalacademy.com
  • 29.  Accommodates shape variability  Tolerant to noise  Techniques are well studied  Large selection of software tools available www.indiandentalacademy.com
  • 30.  Results depend on the training set  Difficult to interpret some results  A number of network parameters, such as topology and number of neurons, must be determined empirically www.indiandentalacademy.com
  • 31.  To describe the techniques used for automatic landmarking of cephalograms,  highlighting the strengths and weaknesses of each one  reviewing the percentage of success in locating each cephalometric point www.indiandentalacademy.com
  • 32.  The literature survey was performed by searching  Medline  Institute of Electrical and Electronics Engineers  ISI Web of Science Citation Index databases www.indiandentalacademy.com
  • 33.  The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus.  The original articles were then retrieved. Their references were also handsearched for possible missing articles. www.indiandentalacademy.com
  • 34.  Report of mean error between real position and estimated position of landmark for each point  Data in millimeter  Articles in English  Articles published from January 1966 to August 2006 www.indiandentalacademy.com
  • 35.  Review articles, abstracts and letters  Data in pixel  Total mean error of the method for a large set of landmarks  Descriptive methods  Computer assisted method  Only graphic data on accuracy of landmark location  Recognition rate presented as percentage of success  Automatic measurements not landmarks  Cephalometric points not stated  Not every landmark detection is a cephalometric point www.indiandentalacademy.com
  • 36. www.indiandentalacademy.com
  • 37. www.indiandentalacademy.com
  • 38. Soft-computing or learning approach www.indiandentalacademy.com
  • 39. www.indiandentalacademy.com
  • 40. www.indiandentalacademy.com
  • 41. Why increased demand?  Advances  Affordability in digital radiographic imaging www.indiandentalacademy.com
  • 42. this literature review……..  many studies seemed to be methodologically unsound  inclusion criteria of patient radiographs  the number of radiographs used,  the error level to create a comparison with the absence of any standard deviation of the mean error www.indiandentalacademy.com
  • 43. marked difference in results… Heterogeneity in the performance of techniques to detect the same landmark  Sella Point : Hybrid approaches > model-based approach   can be due to the high variability of the shape of sella www.indiandentalacademy.com
  • 44. marked difference in results… Porion, gonion and anterior nasal spine higher precision by the hybrid approach  Nasion - nearly the same  hybrid techniques –   better results,  accuracy close to the one suitable for clinical practice www.indiandentalacademy.com
  • 45. Discussion  Recommended total error  x coordinate - 0.59 mm  y coordinate - 0.56 mm  Euclidian value of error should be 0.81 mm  amazing values for standard errors and standard deviations that are far from standard errors for landmark identification www.indiandentalacademy.com
  • 46.  2 mm difference between the location of landmark, obtained by some automatic method and that obtained by the human operator, has been considered by most people to be successful 4 mm distance acceptable  Conclusions drawn from the studies – optimistic than reality  www.indiandentalacademy.com
  • 47.  if one considers that two cephalometric points are needed to trace a reference plane or line, the resulting special position of the line will be affected by the errors of two points, not a single one, and thus the error will be increased. www.indiandentalacademy.com
  • 48.  studies presenting an agreement between manual and computer-assisted methods in millimeters, most consider the Euclidian value, and do not refer to the x-axis and www.indiandentalacademy.com
  • 49.  Automatic landmarking is the first and last step in the development of a completely automatic cephalometric analysis. www.indiandentalacademy.com
  • 50.  Four broad categories  image filtering plus knowledge- based landmark search  model-based approaches  soft-computing approaches  hybrid approaches www.indiandentalacademy.com
  • 51.  The systems described in the literature are not accurate enough to allow their use for clinical purposes as errors in landmark detection were greater than those expected with manual tracing www.indiandentalacademy.com
  • 52.  The ability to automatically identify landmarks is fair for many landmarks, but for routine clinical use it must be reliable  It should be emphasized that if automatic land marking shall be used, it has to be with respect to validity, reliability, and costs www.indiandentalacademy.com
  • 53. www.indiandentalacademy.com
  • 54. Thank you For more details please visit www.indiandentalacademy.com www.indiandentalacademy.com

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