For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation, and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training, and weight training. Next, we automatically segment the tooth contours in an energy-minimized manner. Finally, the automatic assessment of orthodontic parameters is carried out. The experimental results show that the average of absolute distance, the Dice similarity coefficient, and the average qualitative score ranged between 4.17 and 6.03, 0.87 and 0.90, as well as 2.58 and 3.12, respectively. The orthodontic assessment also is close to the evaluation of orthodontists. It has been shown that the proposed method can obtain accurate and consistent measurement in helping dentists to obtain an objective treatment evaluation.

Watershed transformation has proven to be an important tool in image analysis. However, the resulting image of watershed transformation is inevitably over-segmented due to the presence of noise or local irregularities in the input image. In this paper, the use of contour altitude at the immersion stage is proposed. Block gradient information computed from the input gradient image is defined and used to obtain a critical altitude of watershed flooding. This altitude is then refined based on entropy estimated from the intermediate segmentation result. Thereafter, an optimal altitude and its corresponding segmentation result can be obtained. Although this process can favorably reduce the number of regions, the quality of segmentation still requires further improvement. Hence, a Markov Random Field (MRF) model defined on a region adjacency graph (RAG) is adopted. Because the MRF model can merge neighboring regions that are similar in local statistic properties, it thus alleviates the over-segmentation problem and improves the quality of image segmentation. In the experimental studies, the proposed method has been tested using several benchmark images. It achieves improved appearance and energy indices in comparison with the results obtained by conventional methods.

A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.

A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu–Seneviratne algorithm, 24 the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.

We describe a low-cost three-dimensional (3-D) virtual colonoscopy system that is a noninvasive technique for examining the entire colon and can assist physicians in detecting polyps inside the colon. Using the helical CT data and proposed techniques, we can three-dimensionally reconstruct and visualize the inner surface of the colon. We generate high resolution of video views of the colon interior structures as if the viewer's eyes were inside the colon. The physicians can virtually navigate inside the colon in two different modes: interactive and automatic navigation, respectively. For automatic navigation, the flythrough path is determined a priori using the 3-D thinning and two-pass tracking schemes. The whole colon is spatially subdivided into several cells, and only potentially visible cells are taken into account during rendering. To further improve rendering efficiency, potentially visible cells are rendered at different levels of detail. Additionally, a chain of bounding volume in each cell is used to avoid penetrating through the colon during navigation. In comparison with previous work, the proposed system can efficiently accomplish required preprocessing tasks and afford adequate rendering speeds on a low-cost PC system.

Facial model reconstruction and surgical simulation are essential to plastic surgery in today's medicine. Both can help surgeons to design appropriate repair plans and procedures prior to actual surgery. In this paper, we exploit a metamorphosis technique in our new design. First, using metamorphosis and vision techniques, we can establish three-dimensional facial models from a given photo. Second, we design several morphing operators, including augmentation, cutting, and lacerating. Experiments show that the proposed algorithms can successfully create acceptable facial models and generate realistically visual effects of surgical simulation.

Metamorphosis is a powerful visual technique, for producing interesting transition between two images or volume data. Image or volume metamorphosis using simple features provides flexible and easy control of visual effect. The feature‐based image warping proposed by Beier and Neely is a brute‐force approach. In this paper, first, we propose optimization methods to reduce their warping time without noticeable loss of image quality. Second, we extend our methods to 3D volume data and propose several interesting warping operators allowing global and local metamorphosis of volume data.

A multiscale corner detection algorithm based on the wavelet transform of contour orientation is proposed. It can utilize both the information of the local extrema and modulus of transform results to detect corners and arcs effectively. The ramp-width of contour orientation profile, which can be computed using the transformed modulus of two scales, reveals the difference between corner and arc and is utilized in the determination of corner points. The experimental results have shown that the detector is more effective than both the single- and multiple-scale detectors. They also demonstrate that the detector is insensitive to boundary noise. In addition, the proposed method is more efficient than the other multiscale corner detector because it operates on fewer number of scales, which can be implemented by a fast transform algorithm.

The measurement of left ventricular function is greatly enhanced when the three-dimensional shape of the ventricle can be closely assessed by biplane angiocardiograms taken from two perpendicular X-ray camera sets. The ventricle shape can be reconstructed by dividing each of the two orthogonal projection images into corresponding slices, processing each pair of corresponding slices to form a binary cross-sectional image, and stacking up these parallel cross-sectional images. Existing methods of reconstructing cross sections often involve assumptions of elliptic shape or convex symmetry. Recently, a new approach was proposed to reconstruct each cross section by estimating an equal-divisor curve instead of estimating the boundary directly. The approach works well as long as a certain regularity condition (a much relaxed assumption) is satisfied. The notion is extended to the equal-divisor surface of a three-dimensional object with two orthogonal projections. A method for the direct three-dimensional reconstruction of the left ventricle (instead of piling up the parallel reconstructed two-dimensional cross sections) is given.>

A precision digital edge-line-detection method is presented that was developed for extracting edge contours of resist lines of submicrometer width as imaged by scanning electron microscopy, as a means of inspection in integrated circuit fabrication. The method is based on a modification of the Hough transform.>

A new method for reconstruction of the left ventricle from the biplane angiocardiograms is proposed. By dividing the two projection images into parallel slices, the ventricular reconstruction is processed slice by slice stepwise. Each corresponding pair of slices form two one-dimensional projection profiles used to reconstruct a ventricular cross section. Without using predefined binary mask models, we propose to reconstruct the cross section under the assumption that the cross section is regular and a monotonically nondecreasing or nonincreasing equal-divisor curve is available. Instead of solving the binary matrix directly, we need only to obtain the equal-divisor curve. Then the cross section is uniquely determined. The experiments show better results than by any existing direct methods. It also prevents the sensitive selection of mask models.

The problem of speedily and reliably interpreting range data for industrial object recognition is becoming critically important in the field of robotics and computer vision. This paper presents one approach to attack this problem. It attempts to perform as much classification analysis as possible during the off-line learning process, while only a very small subset of the range data needs to be processed for the on-line recognition. An adaptive matched filter is developed to extract an object's surface feature vectors using range measurement and surface normals. A sparse representation for each object category j is constructed for classification purpose in the form of a Rj-table of selected feature vectors. The approach, based on the concept of the generalized Hough Transform, classifies an object by examining the maximum votes which it receives from various Ri-tables. An optimal selection rule is established for minimizing the misclassification probability. An experiment with a set of simulated range images of nine categories demonstrated the success of the proposed methodology.

This paper presents our progress on automated inspection of VLSI resist patterns as imaged by scanning electron microscopy. A precision digital edge line detection method has been developed for extracting edge contours of resist lines of submicron width, which are crucial for micro registration and detection of defects including bubbles and holes, resist stringers and resist edge defects.