A new dental panoramic X-ray image registration technique using hybrid and hierarchical strategies

Mekky, Nagham; Abou-Chadi, Fatma E. Z.; Kishk, Sherif

doi:10.1109/icces.2010.5674886

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The output of this study can be used in various scenarios, such as improving registrations of panoramic x-rays through the information gained from a segmented mandible. 3 It can affect dental biometrics, which has become a popular field of research. It is also a promising method for human identification.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Automatic detection of maxillofacial landmarks in lateral cephalometric radiography has recently attracted some attention; 1,2 whereas other researchers focused on panoramic x-rays (i.e., orthopantomogram) and introduced methods for their registration to give clinicians the complementary information provided by different modalities, 3 extracted the region of interest of each tooth by means of vertical and horizontal integral projections, 4 or presented methods for detection of carotid artery calcification. 5 Segmentation of anatomical structures is a vital step for many applications.…”

Section: Introductionmentioning

confidence: 99%

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Automatic segmentation of mandible in panoramic x-ray

2015

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As the panoramic x-ray is the most common extraoral radiography in dentistry, segmentation of its anatomical structures facilitates diagnosis and registration of dental records. This study presents a fast and accurate method for automatic segmentation of mandible in panoramic x-rays. In the proposed four-step algorithm, a superior border is extracted through horizontal integral projections. A modified Canny edge detector accompanied by morphological operators extracts the inferior border of the mandible body. The exterior borders of ramuses are extracted through a contour tracing method based on the average model of mandible. The best-matched template is fetched from the atlas of mandibles to complete the contour of left and right processes. The algorithm was tested on a set of 95 panoramic x-rays. Evaluating the results against manual segmentations of three expert dentists showed that the method is robust. It achieved an average performance of [Formula: see text] in Dice similarity, specificity, and sensitivity.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of mandible in panoramic x-ray

2015

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“…CT and MR images 3D [46] ,the proposed method results in 0.7805 normalized cross-correlation coefficient (NCCC) and 0.1040% percentage relative root mean square error (PRRMSE) [42].…”

Section: B) At Optimization Levelmentioning

confidence: 99%

Hybrid image registration methods: A Review

Kumari¹

2020

IJATCSE

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This paper presents a review of hybrid image registration techniques (methodologies) that have been used in the medical field. This paper aims to present the survey of methods and methodologies available in context to image registration techniques according to a hybrid approach used to get images registered. Image registration is the first step in digital image processing which include the geometric alignment of sensed and reference images. Different areas of image processing like computer vision, remote sensing and medical image analysis have different methods for image registration along with different challenges associated with individuals. In this paper, work related to the hybrid image registration process is presented. In the hybrid approach, the individual superiority or advantage of different methods are combined in order to get the better results in comparison to individual approach.

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