Automatic Land Marking on Lateral Cephalogram Using Single Fixed View Appearance Model for Gender Identification

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1 International Journal Of Engineering And Computer Science ISSN: Volume 2. Issue 10 October 2013 Page No Automatic Land Marking on Lateral Cephalogram Using Single Fixed View Appearance Model for Gender Identification Janaki Sivakumar 1, Prof.K.Thangavel 2 1 Research Scholar, Prist University Tanjore, Tamilnadu 2 Head Of Computer Science, Periyar University Salem, Tamilnadu Abstract: This manuscript describes Active Appearance model for automatic Cephalometric analysis in forensic science, which is concerned with the recognition, identification, individualization, and evaluation of physical evidence. A complete model of shape and texture was built from a dataset of manually annotated images, and then tested with unseen images. To apply AAM Method for identifying landmark points 140 images were collected from AAOF out of it 67 were female and 73 were male with age ranging from 15 years to 45 years. By using the AAM algorithm, the mean shape is extracted and the appearance variation collected by establishing a piece-wise affine warp between each image of the training set and the mean shape. Keywords: Active Appearance Model, Forensic Science, Cephalometric Analysis. 1. Introduction Usually Forensic science is concerned with the recognition, identification, individualization, and evaluation of physical evidence. Computational forensics is the study and application in forensic investigations. This involves the use of computer models and simulations, applying mathematical techniques to quantify evidence and probabilities. Computational forensics is particularly useful in identification of criminal and evidential patterns. Forensic scientists present the findings from Computational forensics,as an expert witness in the court of law. Cephalometry is the study of human head in terms of skull. The dimensions are measured based on Lateral skull X ray views which are called cephalograms.analysis of feature point extarctions from cephalograms is called Land mark identification in Hard and Soft tissue.orthodontics uses soft tissue landmarks like LabraleSuperius, LabraleInferius, SoftTissuePogonion and Pronasale, as computational forensics needs hard tissue land marks like Sella,Nasion,Porion,Orbitale,A Point,B Point Menton and Gonion for crime investigationin the aspects of gender discrimination,age and race. Manual cephalometric annotation had been used widely. It can take an experienced orthodontist up to thirty minutes to analyze one X-ray. Automation of cephalometric analysis will reduce the analysis time and improve the landmark identification. 3.1 Landmark identification from CR cephalograms Manual Method: the oldest and still most widely used method is analyzing CR- Cephalometric images and manually tracing skeletal features identifying feature points, and measuring distances and angles between landmark locations. Drawbacks: In manual approach, errors vary significantly based on feature points, expert who is tracing the landmark points and the quality of the CR-cephalograms. Alsothis is monotonous, time-consuming and error prone approach. Automatic Method: There are various approaches (Table 1)to detect feature points automatically that takes the differences of characteristics in the images to detect feature points. Automatic approaches helps in minimizing time, more accuracy and reducing errors. General standard approach forland mark identificationmeasurements with errors are: landmarks should be located within 1mm tolerance; although 2mm is deemed acceptable for clinical practice Attempts for automatic cephalometric analysis began from 1985 by Hussain et al. [4]. Levy Mandel et al. [5] used line extraction to find the landmarks of these lines. Searches for specific lines in a specified order taking into account the segments that constitute the line and intersection between lines. Parthsarathy et al. [6] used feature recognition techniques to locate the landmarks. A resolution pyramid of three levels was first created. The search begins in course level then moves to Janaki Sivakumar, 1 IJECS Volume 2. Issue 10 October 2013 Page No Page 3076

2 high resolution to reduce the time. Five cephalograms (480 X 512) with 256 grey levels had been searched for 9 landmarks. The error exceeded 3 mm on average. An interesting table for the difference between the experts themselves had been presented. It has been suggested that an error of 2 mm in landmark is acceptable [7]. But an accuracy of 1 mm is desirable [8]. Forsyth and Davis [8] used ten cephalograms scanned at 512 X 512 pixels with 64 grey levels. On average 63 percent of 19 landmarks were located within 1mm and 74 percent to within 2 mm. The conjunction between image processing techniques and prior knowledge of the cranial structure had been used in all these earlier works. These techniques were limited to small dataset of images. So the underlying flaw is that they had to handle variation in biological shapes and quality in coding the algorithm. A learning approach for automatic landmarking gave more accurate results than the earlier handcrafted algorithms. Cardilllo and Sid -Ahmed [9] located 76% of 20 landmark with 2 mm in a training set of 40 images (512 X 480) pixel with 256 grey levels. They used sub-image matching on hand selected features. Hutton,T.J et al. [10] used Active Shape Model (ASM) on a large dataset. Average error of 4mm of 16 landmarks was achieved. Chakrburthy et al. [11] used support vector machines. 70 image (760 X 500) for training and 40 images for testing. 8 landmarks were used. Accuracy of 95% can be achieved for most landmarks given a larger tolerance value of 5 mm. Innes et al. [12] used pulse coupled Neural Network (PCNN). The output of PCNN was used by a subsequent process such as curve following of Hough transform to identify the positions cephalometric landmarks. 109 images had been used with accuracy of 93.66% one landmark on soft tissue, 36.6% of 2 landmarks on bony structure. Automation of finding parameter values of PCNN processing will be useful for the different landmarks. Romaniuk et al. [13] used non-linear Principle Component Analysis with Average Mean of 3.3 mm. Recently, Ciesielsk et al [14] used Genetic programming for four landmarks using 100 images. Active Appearance Model (AAM) was described by Cootes et.al [15]. A direct extension of the ASM approach used in[10]. Besides shape information the textual information (i.e. the pixel intensities across the object) is included into the model. The aim of this study was to evaluate usage of AAM followed by simulated Annealing for automatic cephalometric analysis. 2. Active Appearance Model AAM is an hybrid approach which involves Average shape(model Based Approach) and training set(soft Computing Approach). Here the term Appearance includes both shape and texture. Shape is a metric which denotes characteristic location from the image with some allowable basic transformations like rotation,scaling andtranslation. Texture involves intensity of an image in a normalized frameupto some offset values. To apply AAM Method for identifying landmark points, 140 images were collected from AAOF collection out of it 67 were female and 73 were male with age ranging from 15 years to 45 years.aam single view drop out method is implemented by using Matlab.Each image has a sizeof pixels and 256 grey-levels. 51 land marks were considered out of these 51 points 8 points are primary cephalometric landmarks and the remaining 43 points were considered as auxiliary points. These cephalograms were scanned in using a Microtek Scan Maker 4 flatbed scanner at 100 dpi ( 1 pixel = 0.25 mm). All the images were padding with black valued pixels from the right and the bottom to make all the images have the same size (794 X 1042) with 256 grey levels which is crucial for AAM to work. Twenty images were used for building the model and seven images were used for testing the model. This small data set was used for the comparison between the two techniques and testing the whole set will be achieved later.et: 3. Structure of Appearance Model Principal component analysis is used to find the mean shape and main variations of the training data to the mean shape. After finding the Shape Model, all training data objects are deformed to the main shape, and the pixels converted to vectors. Then PCA is used to find the mean appearance (intensities), and variances of the appearance in the training set. Both the Shape and Appearance Model are combined with PCA to one AAM-model. By displacing the parameters in the training set with a know amount, an model can be created which gives the optimal parameter update for a certain difference in model-intensities and normal image intensities. This model is used in the search stage. Figure 1: An Appearance Model Structure According to shape definition by Kendal [16], shape is invariant to Euclidean transformations (i.e. translation, scaling and rotation need to be filtered out). All the samples were aligned to the mean shape iteratively described by Goodall [17].Principal Component Analysis (PCA) was performed after alignment of all shapes to the mean shape. PCA was used asa dimensionality reduction technique by producing a projection of a set of multivariate samples into a subspace constrained to Janaki Sivakumar, 1 IJECS Volume 2. Issue 10 October 2013 Page No Page 3077

3 explain a certain amount of the variation in the original samples. The method represents landmark points, (xi, yi), for each image as a 2n vector, x, where x = (x1,...,xn, y1,..., yn) T will describe the shape of an object. The method performs a Principal Component Analysis (PCA) on the aligned training set to describe the shape and appearance variations (texture) of the object. Then apply PCA in training set as follows: Model of the shape is performed as Figure 2: System Architecture, s E s V s is the mean Eigen Vector Matrix Model Vector Model of the Texture (Gray) is performed as To apply AAM Method for identifying landmark points, 140 images were collected from AAOF collection out of it 67 were female and 73 were male with age ranging from 15 years to 45 years.aam single view drop out method is implemented by using Matlab.Each image has a sizeof pixels and 256 grey-levels. 51 land marks were considered out of these 51 points 8 points are primary cephalometric landmarks and the remaining 43 points were considered as auxiliary points. Below are the primary cephalomatric landmarks to identify gender. Model and texture parameter variance are determined by the Eigenvalues Often, shape and texture are correlated. For combining shape and texture, joint parameter vector bi is used in every pair (xi,yi) of the training set as W st distances (shape) and pixel intensities (texture) Apply PCA to the training set is a suitable Weight matrix between the pixel Finally the combined model is represented by combined parameter C Figure 3: Cephalometric Landmarks 3.1 Method to Identify Reference points Sella (S) the center of the hypophyseal fossa (sella tursica). Nasion (Na) the junction of the nasal and frontal bones at the most posterior point on the curvature of the bridge of the nose. A-point (A) an arbitrary measure point on the innermost curvature from the maxillary anterior nasal spine to the crest of the maxillary alveolar process. A-point is the most anterior point of the maxillary apical base. B-point (B) An arbitrary measure point on the anterior bony curvature of the mandible. B point is the innermost curvature from chin to alveolar junction. Menton (Me) the lowest point on the symphysis of the mandible. Anterior Nasal Spine (ANS) the most anterior point on the maxilla at the nasal base. Orbitale (Or) a point midway between the lowest point on the inferior margin of the two orbits. Janaki Sivakumar, 1 IJECS Volume 2. Issue 10 October 2013 Page No Page 3078

4 Gonion a point midway between the points representing the middle of the curvature at the left and right angles of the mandible. Articulare (Ar) a point midway between the two posterior borders of the left and right mandibular rami at the intersection with the basilar portion of the occipital bone. Porion (Po) the midpoint of th upper contour of the external auditory canal (Anatomic Porion) or a point midway between the top of the image of the left and right ear-rods of the cephalostat (Machine Porion). Figure 2: Landmarks -51 points Step 2: After the training set is made ready that will be used (MATModel file) in ModelAAM to create average shape and Texture (Figure 1 An Appearance Instance & Figure 3 : Mean Shape) Figure 4: An Appearance Instance For applying AAM methodology, the well known scheme called drop out method was used. This approach uses M-N images for training and N images are used to test the performance of AAM. In this approach, three sets of enhanced images were used for AAM Approach Mean Filtered image Data Set: Out of 67 female and 73 male data sets, first 20 images are used for training purpose on each. Weiner Filtered Image Data set: Out of 67 female and 73 male data sets, middle 20 images are used for training purpose on each. GaussianFiltered Image Data set: Out of 67 female and 73 male data sets, last 20 images are used for training purpose on each. Figure 3: Mean Shape Step 3: Fit GUI use each image in the image dataset to compare with trained set and automatically mark 51 points that will be stored in.asf file as wordpad file(figure 4 Input mask) By using the above explained AAM algorithm, the mean shape is extracted and the appearance variation collected by establishing a piece-wise affine warp (based on the Delaunay triangulation) between each image of the training set and the mean shape. 3.2 Method implementation Step1: First By using AnnotateGUI, annotate 51 points in each image of the training setmale 15, Female 15(Figure 2 Landmarks -51 points) Figure 5: Input mask 4. Conclusion Automatic Cephalometric analysis becomes an important issue for time saving and robust landmarks identification. AAM provides a compact model, which takes advantage of all the information available by landmark locations and pixel Janaki Sivakumar, 1 IJECS Volume 2. Issue 10 October 2013 Page No Page 3079

5 intensities. The results show that AAM followed by PCM can give more accurate results than ASM used by Hutton, T.J [7] because the same dataset had been studied. But due to the large memory requirement of AAM, we didn t use all of the available 140 images. Further we can incorporate a compression technique such as wavelets for modeling the texture variations in AAM as suggested by Stegmann et al. [18]. From the nature of multivariate linear Regression, It can be seen that a large number of experiments during the building model constitutes a better predication model so if we increase the number of training images we can get more accurate results. To improve the efficiency and robustness of the algorithm, it can be implemented in a multi-resolution framework. Searching begins in a coarse image then refining the location in a series of finer resolution images. This will lead to a faster searching and less likely to get stuck on the wrong image structure.rentheses. References [1] Janaki Sivakumar, Multi Variant Analysis and validation study on Mastoid Triangle Process for Gender Identification in IJITR, Volume No. 1, Issue No. 2, February-March 2013, ISSN No: [2] Janaki Sivakumar, Computed radiography skull image enhancement using Wiener filter IEEE Xplore, March [3] M. Arulselvi "Cephalometric Analyis Using PCA and SVM" International Journal of Computer Application, Volume 30- No.4 September [4] Hussain, Z. and Ip, H.H.S. Automatic identification of cephalometric features on skull radiographs. ACTA polytechnica Scandinavica-Applied physics series, (150), , [5] Levymandel, A.D., Venetsanopoulus, A.N. and Tsutos, J.K. Knowledge-based landmarking of cephalograms. Computers and Biomedical Research, 19 (3), , Jun [6] Parthasarathy, S., Nugent, S.T., Gregson, P.G. and Fay, D.F. Automatic landmarking of cephalograms. Computers and Biomedical Research, 22 (3), , Jun [7] Rakosi, T. An atlas of cephalometric radiography, Wolfe Medical Publications, London, [8] Forsyth, D and Davis, D.N. Assessment of automated cephalometric analysis system. European Journal of Orthodontics, 18 (5), , Oct [9] [6] Cardillo, J. and Sid-Ahmed, M.A. An image processing system for locating craniofacial landmarks, IEEE transaction on Medical Imaging June 1994, 13 (2), , [10] Hutton, T.J., Cunningham, S., and Hammond, P.An evaluation of active shape models for the automatic identification of cephalometric landmarks. European Journal of Orthodontics 22(5): , Oct [11] Shantunu chakrbarthy, Masakazu Yagi, Fadashi Shibata and Gert Cauwenberghs Robust Cephalometric landmark identification using Supper Vector Machines. [12] Andrew Innes, Vic Ciesielski, John Mamutil and Sabu John,Landmark Detection for cephalometric Radiology images using Pulse Coupled Neural Network (PCNN) Proceedings of the international conference on Artificial Intelligence, volume 2 pages las Vegas, June [13] B. Romaniuk, M. Desvignes, M. Revenu, M. J. Deshayes.Linear and Non-Linear Model for Statistical Localization of Landmarks In 16 th International Conference on Pattern Recognition.(ICPR'02) Volume 4, 2002 Quebec City, QC, Canada. [14] Vic Ciesielski, Andrew Innes, Sabu John and John Mamutil, Genetic programming for landmark detection in cephalometric Radiology Images International Journal Of knowledge-based Intelligent Engineering System vol 7, No 3, july [15] T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. In Proc. European Conf. on Computer Vision, volume 2, pages Springer, [16] L. Dryden and K. V. Mardia. Statistical Shape Analysis. John Wiley & Sons, [17] C. Goodall. Procrustes methods in the statistical analysis of shape.jour. Royal Statistical Society, Series B, 53: , [18] M.B.Stegmann,S.Forchhammer,and T.F.Cootes. Wavelet enhanced appearance modelling. In International Symposium on Medical Imaging 2004, San Diego CA, SPIE.SPIE, [19] Predrag Vucinic "Automatic Landmarking of Cephalograms Using Active Appearance Models" Europian Journal of Orthodontics, March [20] Rosalia Leonardi "An Evaluation of Cellular Neural Networks for the Automation Identification of Cephalometric Landmarks on Digital Images" Journal of Biomedicine and Biotechnology, Vol 2 Issue 3, March [21] Shantanu Chakrabartty "Robust Cephalometric Landmark Identification Using Support Vector Machines" Journal of Computerscience, March Janaki Sivakumar, 1 IJECS Volume 2. Issue 10 October 2013 Page No Page 3080