A Fast Serial and Parallel Thinning Algorithm

Wang, P. S. P.; Zhang, Y. Y.

doi:10.1007/978-94-009-4634-7_117

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…For example, the molar area (Figure 5c) is nearly vertical; the crown Detection of the dental arch curve based on mask images. Firstly, the mask image (Figure 4a) is processed by the thinning algorithm [29][30][31], and the bifurcation endpoint template of 8 fields is added to compare the skeleton line in the refinement calculation process (Figure 4b), the bifurcation and endpoint structure in the skeleton are recorded, and the branching statistics and fork removal are performed (Figure 4c). Then, the insertion point is determined on the skeleton.…”

Section: Dental Arch Curve Detectionmentioning

confidence: 99%

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

et al. 2022

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Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient’s mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast.

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Section: Dental Arch Curve Detectionmentioning

confidence: 99%

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

et al. 2022

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“…In fast Parallel Thinning Algorithm, the method for extracting the skeleton of a picture consists of removing all the contour points of the picture except those points that belong to the skeleton [16] [18]. In order to preserve the connectivity of skeleton, each iteration is divided into two sub-iterations [17].…”

Section: Voronoi Diagrammentioning

confidence: 99%

Topology Preserving Skeletonization Techniques for Grayscale Images

Krishna¹,

Gangadhar²,

Neelima³

et al. 2016

Computation and Communication Technologies

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Thinning is an interesting and challenging problem, and plays a central role in reducing the amount of information to be processed during pattern recognition, image analysis and visualization, computer-aided diagnosis. A topology preserving thinning algorithm is used which removes the pixels from a gray scale images. First it checks for the condition whether a pixel is acyclic or not. If a pixel is acyclic, then it is removed from the image else retains in the image. A topology preserving skeleton is a synthetic representation of an object that retains its shape, topology, geometry, connectivity and many of its significant morphological properties. It has been observed in our experiments that thinning algorithm is stable and robust and yield promising performance for wide range of images.

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“…After the binarized image is obtained, the next and most important step is to thin the image -i.e., to make the ridges of the image as thin as 1-pixel, without disturbing the basic structure and details of the image. The thinning algorithms devised to date [1][2][3][4][5][6][7] do not provide positional and rotational invariance, nor are they appropriate for removing lakes and island type regions from the fingerprint.…”

Section: Maria: Minutia Analysis Using Rotation Invariant Algorithmmentioning

confidence: 99%

MARIA: Minutiae Analysis using Rotation Invariant Algorithm

Pokhriyal¹,

Lehri²

2010

IJoFCS

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The process of fingerprint thinning creates a 1-pixel wide pattern of the fingerprint ridges while retaining the basic fingerprint structure. Several algorithms have been devised [1 to 7] to extract the thinned pattern of a fingerprint image; however, such algorithms produce different results with different positions and rotations of the same fingerprint image, which results in inefficient minutia extraction. In this paper, a new method of fingerprint thinning is proposed: Minutia Analysis using Rotation Invariant Algorithm (MARIA), which thins a fingerprint image regardless of the fingerprint position and rotation and then extracts minutia from the thinned pattern. The fingerprint image is binarized to convert it into a 0-1 pattern. Morphological operations are applied to remove islands and lake-like portions from the image. If-then rules governing a 3x3 mask that is to be convoluted throughout the image are applied to thin the transformed reduced image in a rotation-invariant manner. Post processing is performed on the thinned image to remove any spurious minutia points. Finally, genuine minutia points and their directions are extracted from the thinned fingerprint image. The experimental results indicate that MARIA successfully extracts genuine minutiae from low-quality fingerprint images, and greatly facilitates the minutia matching procedure.

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A Fast Serial and Parallel Thinning Algorithm

Cited by 11 publications

References 7 publications

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

Topology Preserving Skeletonization Techniques for Grayscale Images

MARIA: Minutiae Analysis using Rotation Invariant Algorithm

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