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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
templates Experimental evaluationResultsConclusions
A cephalogram
Tracing key anatomical structures
Landmarks identification
Baseline for measurements
Approaches to cephalometrics
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
templates Experimental evaluationResultsConclusions
1. speed-up a very time-consuming manual process
2. improve measurements accuracy
AIME 05
Why automated landmarking?
Literature review Outlines of CNNs. Tool and the CNN
templates Experimental evaluationResultsConclusions
PRIOR KNOWLEDGE LEARNING APPROACH
AIME 05
Previous approaches to automated landmarking
Literature review Outlines of CNNs. Tool and the CNN
templates Experimental evaluationResultsConclusions
1. Use of filters to minimize noise and enhance the image,
2. Application of operators for edge detection,
3. On line-following algorithms guided by a prior knowledge, introduced in the system by means of simple ad hoc criteria
AIME 05
Approaches based on prior knowledge
Literature review Outlines of CNNs. Tool and the CNN
templates Experimental evaluationResultsConclusions
Some examples of the techniques that have been used:
• Neural networks together with genetic algorithms
• Fuzzy neural networks• Active shape models
AIME 05
Approaches based on learning and pattern matching
Literature review Outlines of CNNs. Tool and the CNN
templates Experimental evaluationResultsConclusions
Work Sample size
Techniques
Parthasarathy et al.
(1989)
5 Resolution piramidKnowledge based line
extractor
Tong et al. (1990)
5 Resolution pyramid Edge enhancementKnowledge-based extraction
Cardillo et al.(1994)
40 Pattern matching
Rudolph et al.(1998)
14 Spatial spectroscopy Statistical pattern
recognition
Liu et al.(1999)
38 Multilayer Perceptron Genetic Algorithms
Hutton et al.(2000 )
63 Active Shape Models
El-Feghi et al.(2003)
200 Fuzzy neural network
Innes et al.(2002)
109 PCNN pulse coupled neural networks
Limitations of previous approaches
1. Accuracy achieved
2. Performance varying on different landmarks
3. Strongly dependent on the quality of the X-rays
Golden standard: landmarks should be located within 1mm tolerance; although 2mm is deemed acceptable for clinical practice
AIME 05
Literature review Outlines of CNNs. Tool and the CNN
templates Experimental evaluationResultsConclusions
Our approach
• The proposed method proposed is based on CNN (Cellular Neural Networks)
• CNNs are an emerging paradigm for image processing
• CNNs is a powerful computational model equivalent to a Turing Machine
AIME 05
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
Cellular Neural Networks
• CNN consist of computational units (cells) arranged in matrix forms (2D) or cube forms (3D)
• Each cell is a dynamic unit with an input, an output and a state
• Each cell is influenced by the input and the output of all neighboring cells within a given radius
AIME 05
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
The neighborhood circle of the interacting cells is defined as follows:
Nr (i,j) = C (k,h): max ( k-i , h-j ) ≤ r, 1≤ k≤ M; 1≤ h≤ N
where M and N are the matrix dimensions
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Circles of influence with radius equal to one for cells
Cij, Ci+1, j+1
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
Cellular Neural Networks
• CNN dynamics are determined by the following equation, where x is the state, y is the output, u is the input,
• xij is the generic cell belonging to the matrix
• Iij is the activating treshhold for each cell.
AIME 05
ItuhkjiBtyhkjiAxxjiNhkC
khjiNhkC
khjiji
rr
)(),,,()(),,,(),(),(),(),(
,
.
,
)11(2
1,,, jijiji xxy
Cellular Neural Networks
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
• CNN dynamics are determined by the following equation, where x is the state, y is the output, u is the input,:
• A is known as feedback template
• B is known as control template
AIME 05
ItuhkjiBtyhkjiAxxjiNhkC
khjiNhkC
khjiji
rr
)(),,,()(),,,(),(),(),(),(
,
.
,
)11(2
1,,, jijiji xxy
Cellular Neural Networks
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
• 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.
Cellular Neural Networks
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
In our work we used:• A constant treshold for each cell
• A circle of influence with radius equal to 1 (A, B: 3X3) and with radius equal to 2 (A, B: 5X5)
• Every cell has an initial state variable equal to zero
• Contour condition uij = 0 (Dirichlet condition)
• Input: the image to be processed
• Symmetrical feedback templates (to ensure steady state)
• Exploitation of the transient solution n. of cycles and integration steps are important for landmark identification
AIME 05
Cellular Neural Networks
Literature review
Outline of CNNsTool and the CNN
templates Experimental evaluationResultsConclusions
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
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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
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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.
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Tool Literature review Outline of CNNs
Tool and CNN templates
Experimental evaluationResultsConclusions
Why n. of cycles are important
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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
ResultsConclusions
Work and Ref. Sample size
N. Landmarks and accuracy Techniques
Parthasarathy et al. (1989) [10]
5 9 landmarks, 58% < 2mm, (18%<1mm)
mean error: 2.06 mm
Resolution piramidKnowledge based line extractor
Tong et al. (1990) [11]
5 17 landmarks, 76%< 2mmmean error: 1.33 mm
Resolution pyramid Edge enhancementKnowledge-based extraction
Cardillo et al.(1994) [13]
40 20 landmarks, 75% < 2mm mean error: not reported
Pattern matching
Rudolph et al.(1998) [14]
14 15 landmarks, 13% <2mm mean error: 3,07 mm
Spatial spectroscopy Statistical pattern recognition
Liu et al.(1999) [6]
38 13 landmarks, 23% < 2mm(8% <1mm),mean error: 2,86 mm
Multilayer Perceptron Genetic Algorithms
Hutton et al.(2000 ) [7]
63 16 landmarks, 35% < 2mm(13% < 1mm)mean error: 4,08
Active Shape Models
El-Feghi et al.(2003) [16]
200 20 landmarks, 90% <2mmmean error: not reported
Fuzzy neural network
Innes et al.(2002) [18]
109 3 landmarks, 72% <2mm, mean error: not reported
PCNN : pulse coupled neural networks
Our Work 26 8 landmarks, 85%<2mm (73% < 1mm)
mean error: 1.07 mm
Cellular Neural NetworksKnowledge based landmark
extraction
The experimental results have shown that of the
sought landmarks 85% are within 2mm
precision, and remarkabily that 73% are
within 1mm.
ResultsLiterature review Outline of CNNsTool and CNN templates Experimental evaluation
ResultsConclusions
Conclusions
• CNNs provide an effective method to pre-process images for automated landmarking
• They are accurate and flexible (integration of edge based and region based methods)
• Their hardware implementation affords real-time performance
Literature review Outlines of CNNs. CNN prototype and the
templates Reports the experimental
evaluationResultsConclusions
The approach that we have employed will be further improved by prior classification on the cases based on:
1. Key morphologies of the skull (e.g., byte typology, shape of anatomical structures)
2. X-ray brightness
ConclusionsLiterature review Outline of CNNsTool and CNN templates Experimental evaluationResults
Conclusions
MANY THANKS
GrazieGrazie