AUTOMATIC LANDMARKING OF CEPHALOGRAMS BY CELLULAR NEURAL NETWORKS D. Giordano 1 , R. Leonardi 2 , F. Maiorana 1 , G. Cristaldi 1 , M.L. Distefano 2 1 Dipartimento di Ingegneria Informatica 2 Clinica Odontoiatrica II - Policlinico University of Catania Italy
40
Embed
AUTOMATIC LANDMARKING OF CEPHALOGRAMS BY CELLULAR NEURAL NETWORKS D. Giordano 1, R. Leonardi 2, F. Maiorana 1, G. Cristaldi 1, M.L. Distefano 2 1 Dipartimento.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
AUTOMATIC LANDMARKING OF CEPHALOGRAMS
BY CELLULAR NEURAL NETWORKS
D. Giordano1, R. Leonardi2, F. Maiorana1, G. Cristaldi1, M.L.
Distefano2 1Dipartimento di Ingegneria Informatica
2Clinica Odontoiatrica II - Policlinico
University of CataniaItaly
Cephalometric analysis
• Cephalograms are lateral skull radiographs taken under standard conditions
• Cephalometric analysis is based on the identification of landmarks, which are used for linear and angular measurements
• It is important for orthodontic planning and treatment evaluation
Literature review Outlines of CNNs. Tool and the CNN
1. Manual. placing a sheet of acetate over the cephalometric radiograph, tracing salient features, identifying landmarks and measuring distances and angles between landmark location.
2. Computer aided. Landmarks are located manually while these locations are digitized into a computer system. The computer then completes the cephalometric analysis.
3. Completely automated. The computer automatically locates landmarks and performs the cephalometric analysis.
AIME 05
Literature review Outlines of CNNs. Tool and the CNN
• Several image processing tasks can be performed by CNNs by programming by templates
• Library of known templates are available
• A key advantage is that the inherently parallel architecture of the CNN can be implemented on chips, known as CNN-UM (CNN Universal Machine) chips allowing computation times three orders of magnitude faster than classical methods.
Our system is based on a software simulator of a CNN of 512X480 cells.
It uses different types of CNNs on the scanned cephalogram
1) first to pre-process the image and eliminate the noise,
2) then to ensure that each landmark region is properly highlighted (by appropriate CNN templates)
3) landmark-specific algorithms including expert rules for point identification are then applied and landmarks coordinates computed
AIME 05
Tool Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
AIME 05
The system operates based on two classes of rules
• Expert rules concerning
where landmark should be located,
• Rules to select the proper CNN template based on local image properties
AIME 05
Tool Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
The tool has been designed to detect 8 landmarks, which are essential to conduct a basic cephalometric analysis:
• Menton, • B point, • Pogonion, • PM point, • A point, • Upper incisal, • Lower incisal, • Nasion.
AIME 05
Tool Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
Why n. of cycles are important
AIME 05
Non saturated CNN Output Saturated CNN Output
Using images with the same brightness simplifies point extraction and emphasize program correctness
Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
Menton
AIME 05
;
00000
00000
00100
00000
00000
A ;
11111
00000
00000
00000
11111
B
Templates and Templates and CNN output for CNN output for Menton Menton (n.cycles=30) (n.cycles=30)
Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
Gnation and B point
Templates and CNN Templates and CNN output for Chin output for Chin Curvature Curvature (n.cycles=30) (n.cycles=30)
;
000
010
000
A ;
110
101
011
B
Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
AIME 05
Up and low Incisors
Templates and CNN output for Templates and CNN output for incisors Curvature (n.cycles=60)incisors Curvature (n.cycles=60)
00000
10001
10001
00000
00000
B
00000
20002
20002
00000
00000
B
Good contrast Good contrast and luminosityand luminosity
Low contrast Low contrast and luminosityand luminosity
Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
Nasion
White nasion Black nasion
Four templates were used
Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
AIME 05
• 8 landmarks were chosen for preliminary assessment of the method, and a set of 97 digital X-rays was landmarked by an expert orthodontist.
Literature review Outline of CNNsTool and CNN templates
Experimental evaluation
ResultsConclusions
Assessment
• The first stage assessed the image output of the CNNs, to verify that it included the sought landmark.
• This was done by visual inspection from the same expert who landmarked the X-rays.
• Over 97 cases, 29 cases (30%) led to CNN outputs in which some edges were overly eroded. This implies that the number of processing cycles in these cases needs to be reduced.
AssessmentLiterature review Outline of CNNsTool and CNN templates
Experimental evaluation
ResultsConclusions
• The second stage evaluated performance of the developed algorithms for 8 landmarks
• Sample of 26 cases randomly selected from the previous one after eliminating the cases that had not been taken into consideration by the algorithms.
AssessmentLiterature review Outline of CNNsTool and CNN templates
Experimental evaluation
ResultsConclusions
The coordinates of each point found by the program were compared to expert landmarking, and the Euclidean distance of the found landmark from the reference
one was computed.
AssessmentLiterature review Outline of CNNsTool and CNN templates
Experimental evaluation
ResultsConclusions
Results
Landmark Mean error(mm)
MD SD ≤1 (mm)
>1;≤2(mm)
Imprecise cases Success Rate
Success Rate
(overall sample)
≤3 (mm)
>3 (mm)
Upper incisor
.48 .25 .60 88% 8% 4% - 96% 92%
Lower incisor
.92 .67 .94 66% 26% 4% 4% 92% 81%
Nasion 1.12 .76 1.11 70% 17% - 13% 87% 81%
A Point 1.34 1.06 .82 58% 21% 17% 4% 79% 73%
Menton .62 .33 .82 85% 7% 4% 4% 92% 92%
B Point 2.00 .42 3.3 71% 8% - 21% 79% 73%
Pogonion .87 .04 1.34 73% 8% 8% 11% 81% 81%
PM Point 1.25 .33 1.68 69% 8% 8% 15% 77% 77%
Literature review Outline of CNNsTool and CNN templates Experimental evaluation