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

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.

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1. speed-up a very time-consuming manual process

2. improve measurements accuracy

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Why automated landmarking?



PRIOR KNOWLEDGE LEARNING APPROACH

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Previous approaches to automated landmarking



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

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Approaches based on prior knowledge



Some examples of the techniques that have been used:

• Neural networks together with genetic algorithms

• Fuzzy neural networks• Active shape models

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Approaches based on learning and pattern matching



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

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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

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Literature review

Outline of CNNsTool and the CNN


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

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Literature review



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




• 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.

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ItuhkjiBtyhkjiAxxjiNhkC

khjiNhkC

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Literature review



• 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

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ItuhkjiBtyhkjiAxxjiNhkC

khjiNhkC

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Literature review



• 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.


Literature review



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

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Literature review



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

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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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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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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



Menton

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B

Templates and Templates and CNN output for CNN output for Menton Menton (n.cycles=30) (n.cycles=30)




Gnation and B point

Templates and CNN Templates and CNN output for Chin output for Chin Curvature Curvature (n.cycles=30) (n.cycles=30)

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A ;

110

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B




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Up and low Incisors

Templates and CNN output for Templates and CNN output for incisors Curvature (n.cycles=60)incisors Curvature (n.cycles=60)

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10001

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Good contrast Good contrast and luminosityand luminosity

Low contrast Low contrast and luminosityand luminosity




Nasion

White nasion Black nasion

Four templates were used




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• 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


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.



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.



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

Documents

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