Improving cephalogram analysis through feature subimage extraction

Automatic point detection on cephalograms using convolutional neural networks: A two-step method

This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to compute various angles essential for diagnosis. Utilizing a custom-built desktop computer with a moderately priced graphics processing unit, cephalogram images were resized to 800×800 pixels. Training data comprised 833 images, augmented 100 times; an additional 179 images were used for testing. Due to the complexity of training with full-size images, training was divided into two steps. The first step reduced images to 128×128 pixels, recognizing all 18 points. In the second step, 100×100 pixels blocks were extracted from original images for individual point training. The program then measured six angles, achieving an average error of 3.1 pixels for the 18 points, with SNA and SNB angles showing an average difference of less than 1°.

Cephalometric landmarks identification using probabilistic relaxation

In this paper, we develop methodology to locate cephalometric landmarks on X-ray images based on probabilistic relaxation, which combines local contextual information from the general shape of the bones of the head (used as measurements specific to each landmark in the form of its shape context) and relational information, expressing the relative position of the landmarks with respect to each other.

An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.

Automatic cephalometric analysis

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.

An image representation algorithm compatible with neural-associative-processor-based hardware recognition systems

A robust image representation algorithm compatible with the VLSI-matching-engine-based image recognition system has been developed. The spatial distributions of four-principal-direction edges in a 64 /spl times/ 64-pels gray scale image are coded to form a 64-dimension feature vector. Since the 2D edge information is reduced to a feature vector by projecting edge flags to the principal directions, it is named the projected principal-edge distribution (PPED) representation. The PPED vectors very well preserve the human perception of similarity among images in the vector space, while achieving a substantial dimensionality reduction in the image data. The PPED algorithm has been applied to medical radiograph analysis, which was taken as a test vehicle for algorithm optimization. The robust nature of the PPED representation has been confirmed by the recognition results comparable to the diagnosis by experts having several years of experience in a university hospital. Dedicated digital VLSI circuits have been developed for PPED vector generation in order to expedite the processing. A test hardware recognition system was constructed using the vector generation circuits, where the analog neural associative processor chip developed in a separate project was employed as a vector-matching engine. As a result, a successful medical radiograph analysis has been experimentally demonstrated using the hardware system. Feasibility of a very low-power operation of the system has been also demonstrated.

An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models

Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications.

Automated 2-D cephalometric analysis on X-ray images by a model-based approach

Craniofacial landmark localization and anatomical structure tracing on cephalograms are two important ways to obtain the cephalometric analysis. In order to computerize them in parallel, a model-based approach is proposed to locate 262 craniofacial feature points, including 90 landmarks and 172 auxiliary points. In model training, 12 landmarks are selected as reference points and used to divide every training shape to 10 regions according to the anatomical knowledge; principle components analysis is employed to characterize the region shape variations and the statistical grey profile of every feature point. Locating feature points on an input image is a two-stage procedure. First, we identify the reference landmarks by image processing and pattern matching techniques, so that the shape partition is performed on the input image. Then, for each region, its feature points are located by a modified active shape model. All craniofacial anatomical structures can be traced out by connecting the located points with subdivision curves according to the prior knowledge. Users are permitted to modify the results interactively in many different ways. Experimental results show the advantage and reliability of the proposed method.

Image processing for craniofacial landmark identification and measurement: a review of photogrammetry and cephalometry

Facial surface anthropometry and cephalometry have been used for many years for the diagnosis of malformations, surgical planning and evaluation, and growth studies. These disciplines rely on the identification of craniofacial landmarks. Methods for 3D reconstruction of landmarks have been introduced, as have image processing algorithms for the automation of landmark extraction. This paper reviews facial surface anthropometry and cephalometry with reference to the image processing algorithms that have been applied and their effectiveness.

Accuracy of computerized automatic identification of cephalometric landmarks

Computerized cephalometric analysis can include both landmark identification and determination of linear or angular measurements. Although its use is time saving compared with a manual method, the accuracy of automatic landmark identification remains unclear. The purpose of this study was to evaluate the accuracy of a computerized automatic landmark identification system that used an edge-based technique. The technique divides the scanned cephalogram into 8 rectangular subimage regions. After the resolution of these subimages is reduced, the edges are detected and the landmarks are located automatically. Thirteen landmarks were selected for assessment on a set of 10 test cephalograms. The results showed that the errors between manual and computerized identification for landmarks were not significantly different (P > .05) for 5 of 13 landmarks: sella, nasion, porion, orbitale, and gnathion. These results suggest that the accuracy of computerized automatic identification is acceptable for certain landmarks only. Further studies to improve the accuracy of computerized automated landmark identification are needed.

Evolutionary computation in medicine: an overview

The term evolutionary computation encompasses a host of methodologies inspired by natural evolution that are used to solve hard problems. This paper provides an overview of evolutionary computation as applied to problems in the medical domains. We begin by outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems. We then describe how evolutionary algorithms are applied to solve medical problems, including diagnosis, prognosis, imaging, signal processing, planning, and scheduling. Finally, we provide an extensive bibliography, classified both according to the medical task addressed and according to the evolutionary technique used.