Reconstruction of Panoramic Dental Images Through Bézier Function Optimization

Amorim, Paulo; Moraes, Thiago; Silva, Jorge Vicente Lopes da; Pedrini, Hélio; Ruben, Rui B.

doi:10.3389/fbioe.2020.00794

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…This improvement can mainly be attributed to use of the LTP layers and the data augmentation technique. The performance of the proposed method is compared with the performance of some methods from the latest study in the literature, such as Neck segmentation using CNNs [38], Intelligent level set [39], Local singularity analysis [40], and Bezier function optimisation [41]. The comparison was carried out on the 2,200 testing images, and the results are stated in Table 1.…”

Section: Resultsmentioning

confidence: 99%

The local ternary pattern encoder–decoder neural network for dental image segmentation

Salih

Duffy

2022

IET Image Processing

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Recent advances in medical imaging analyses, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the centre of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performances in the analysis of medical applications and systems. Deep learning techniques have achieved improved performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps dentists to diagnose dental caries. However, the performance of the deep networks used for these analyses are restrained by various challenging features found in dental carious lesions. Segmentation of dental images is often difficult due to the vast variety of types of topology, intricacies of medical structure and poor image quality caused by conditions such as low contrast, noise, irregular, and fuzzy border edges. These issues are exacerbated by low numbers of data images available for any particular analysis. A robust local ternary pattern encoder-decoder network (LTPEDN) is proposed to overcome dental image segmentation challenges and minimise the computational resources required. This new architecture is a modification of existing methods using an LTP. Images are preprocessed via augmentation and normalisation techniques to increase and prepare the datasets. Thereafter, the dataset input is sent to the LTPEDN for training and testing the model. Segmentation is performed using the nonlearnable layers (the LTP layers) and the learnable layers (standard convolution layers), to extract the ROI of the teeth. The method was evaluated on an augmented dataset of 11, 000 dental images. It was trained on 8, 800 training set images and tested on 2, 200 testing set images. The new method is shown to be 94.32% accurate.

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Section: Resultsmentioning

confidence: 99%

The local ternary pattern encoder–decoder neural network for dental image segmentation

Salih

Duffy

2022

IET Image Processing

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“…CT images have been extensively used in dental imaging diagnostics, and therefore, it is possible to use 3D data from the CT images to reconstruct 2D panoramic dental radiographs that provide a longitudinal view of the jaw bone, avoiding additional exposure to X-rays. An automatic method, introduced in [ 26 ], is used for reconstructing 2D panoramic dental images based on 3D CT images, using Bezier curves and optimization techniques, and obtained very smooth panoramic images with good contrast.…”

Section: Related Workmentioning

confidence: 99%

Dental Images’ Segmentation Using Threshold Connected Component Analysis

Majanga

Viriri

2021

Computational Intelligence and Neuroscience

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Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93 % for both precision and recall values, respectively.

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“…In recent decade, many dental arch detection approaches [4][5][6][7][8][9][10][11][12][13][14] were proposed. These approaches created a 2D mask of jaw bones, and subsequently detected a 2D dental arch from this mask.…”

Section: Introductionmentioning

confidence: 99%

Automatic dental arch detection and panoramic imaging from cone beam computed tomography dataset

Jiang,

Zhang,

Shi

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Panoramic imaging from the cone beam computed tomography (CBCT) dataset mimics the X-ray beam shooting along a dental arch which represents the teeth and jaw bones. This study aims to propose a novel dental arch detection algorithm in three-dimensional (3D) CBCT space based on the parametric equation to reconstruct panoramic images with clear visualization of jaw bones, teeth, and dental pulps. Our method involves two main steps: dental arch detection and panoramic imaging. The first step detects the dental arch using a parametric equation on a specially found plane in 3D CBCT space. Binary masks of jaw bones and teeth are required to fit the parametric equation. We employ deep learning techniques for tooth segmentation and a traditional method for jaw bone segmentation. In the second step, the maximal intensity projection and ray sum projection are applied for panoramic imaging. In experiments, a total of 20 CBCT datasets are used to evaluate the proposed method. 78 CBCT datasets are used to train the tooth segmentation network. Experiment results show that our proposed method enables us detect the dental arch directly in 3D CBCT space, and provides an accurate, effective, and robust solution for CBCT-based panoramic imaging.

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Reconstruction of Panoramic Dental Images Through Bézier Function Optimization

Cited by 19 publications

References 32 publications

The local ternary pattern encoder–decoder neural network for dental image segmentation

The local ternary pattern encoder–decoder neural network for dental image segmentation

Dental Images’ Segmentation Using Threshold Connected Component Analysis

Automatic dental arch detection and panoramic imaging from cone beam computed tomography dataset

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