Shape variability and spatial relationships modeling in statistical pattern recognition

Romaniuk, Barbara; Desvignes, Michel; Revenu, Marinette; Deshayes, Marie-Josèphe

doi:10.1016/j.patrec.2003.10.011

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…The second approach involves describing mean recognition errors, ie, the mean distance between the point provided by an orthodontist(s) and the point determined by the system. [6][7][8]14 The third method is to examine whether the system-identified landmark is located in a circle with a 2-mm radius. 2,3,7,[9][10][11][12]15,16 This approach is meaningful in the sense that it provides an objective judgment as to whether or not the system's recommendation is correct, but it leaves room for argument as to whether it is reasonable to apply a circle with a 2-mm radius to all cephalometric landmarks, given that such landmarks are located in varying anatomic structures.…”

Section: Discussionmentioning

confidence: 99%

“…1-8 Yamadaoka, Suita, Osaka, Japan 565-0871 (e-mail: opam@dent.osaka-u.ac.jp) mated clinical examination of cephalograms would reduce the workload during routine clinical service and would provide orthodontists with more time for optimum treatment planning. Various methods such as a knowledge-based technique with edge tracking, [1][2][3][4] model-based approaches, [5][6][7][8] pattern-matching techniques, [9][10][11] and combined algorithms [12][13][14][15][16] have been developed and are available. However, most of these methods have not been adopted in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Automated Cephalometry: System Performance Reliability Using Landmark-Dependent Criteria

Tanikawa

Yagi

Takada

2009

The Angle Orthodontist

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Objective:The purpose of the present study was to evaluate reliability of a system that performs automatic recognition of anatomic landmarks and adjacent structures on lateral cephalograms using landmark-dependent criteria unique to each landmark. Materials and Methods: To evaluate the reliability of the system, the system was used to examine 65 lateral cephalograms. The area of each system-identified anatomic structure surrounding the landmark and the position of each system-identified landmark were compared with norms using confidence ellipses with ␣ ϭ .01, which were derived from the scattergrams of 100 estimates obtained according to the method reported by Baumrind and Frantz. When the system-identified area overlapped with the norm area, anatomic structure recognition was considered successful. In addition, when the system-identified point was located within the norm area, landmark identification was considered successful. Based on these judgment criteria, success rates were calculated for all landmarks. Results: The system successfully identified all specified anatomic structures in all the images and determined the positions of the landmarks with a mean success rate of 88% (range, 77%-100%). Conclusion: With the incorporation of the rational assessment criteria provided by confidence ellipses, the proposed system was confirmed to be reliable.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Cephalometry: System Performance Reliability Using Landmark-Dependent Criteria

Tanikawa

Yagi

Takada

2009

The Angle Orthodontist

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“…All in all, these approaches can be classified into four broad categories, based on the techniques, or combination of techniques that have been employed. These categories are: (1) image filtering plus knowledge-based landmark search [16][17][18][19][20] ; (2) model-based approaches 13,[20][21][22][23][24] ; (3) soft-computing approaches [25][26][27][28][29][30][31] ; and (4) hybrid approaches. [32][33][34][35][36][37] The relative advantages and disadvantages of the technical approaches used to automatically identify cephalometric landmarks are summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cephalometric Analysis

Leonardi

Giordano

Maiorana

et al. 2008

The Angle Orthodontist

122

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Objective: To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. Materials and Methods: The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. 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 hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. Results: A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and ''soft-computing'' approaches. Conclusions: The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low.

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“…The automated approaches can be classified into four broad categories, based on the techniques, Leonardia R et al [54] mentioned these categories with techniques examples for each approach recorded by different authors: 1. Image filtering plus knowledge-based landmark search; [55][56][57][58] 2. model-based approaches [59][60][61][62][63][64] 3. soft-computing approaches [65][66][67][68] 4. hybrid approaches. [69][70][71][72][73] the relative advantages and disadvantages of these technical approaches used in the automated identification of cephalometric landmarks; Image filtering plus knowledge-based landmark search are list in table 1.…”

Section: Cephalometrics Analysismentioning

confidence: 99%

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

Bahaa¹,

Noor²,

Yousif³

2011

Principles in Contemporary Orthodontics

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Shape variability and spatial relationships modeling in statistical pattern recognition

Cited by 15 publications

References 8 publications

Automated Cephalometry: System Performance Reliability Using Landmark-Dependent Criteria

Automated Cephalometry: System Performance Reliability Using Landmark-Dependent Criteria

Automatic Cephalometric Analysis

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

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