Oral and Dental Spectral Image Database—ODSI-DB

Hyttinen, Joni; Fält, Pauli; Jäsberg, Heli; Kullaa, Arja; Hauta-Kasari, Markku

doi:10.3390/app10207246

Cited by 19 publications

(18 citation statements)

References 22 publications

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“…Here, 300 × 400 region-of-interest (ROI) image for each image is chosen from the available dental images [9]. First the training set is created using the caries dataset.…”

Section: ░ 4 Resultsmentioning

confidence: 99%

“…The studies conclude that the Computational intelligence techniques are used in different purposed in Orthodontics. They imitate the accuracy of trained dentists and in some cases, it can be seen that their performance is better than the experts of the field [9,10,11]. The numerous data on malocclusions can be fed to the neural network in case of unsupervised learning and the computers predicts the data.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

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Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Alphonse

Kumari²,

Priyanga

2022

IJEER

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Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process. The proposed MDP extracts both intensity and edge information and creates the feature vector that increases the classification accuracy during caries detection.

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“…Here, 300 × 400 region-of-interest (ROI) image for each image is chosen from the available dental images [9]. First the training set is created using the caries dataset.…”

Section: ░ 4 Resultsmentioning

confidence: 99%

Section: ░ 1 Introductionmentioning

confidence: 99%

Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Alphonse

Kumari²,

Priyanga

2022

IJEER

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“…The reflectance spectral images were obtained from the oral and dental spectral image database dataset [ 40 ]. The Computational Spectral Imaging Laboratory published these images at the University of Eastern Finland, Joensuu campus (Joensuu, Finland), and the Institute of Dentistry at the University of Eastern Finland Kuopio campus (Kuopio, Finland).…”

Section: Methodsmentioning

confidence: 99%

“… A sample of the target class for

patch images, adapted from the image of ODSI-DB [ 40 ] (CC BY-NC-SA 4.0). …”

Section: Figurementioning

confidence: 99%

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions

et al. 2022

Self Cite

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Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant .

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“…Preprocessing of the hypercube data was performed as outlined in the paper (Fabelo et al 2019). Hyttinen et al (2020) provide the second hyperspectral dataset used in this work. The Oral and Dental Spectral Image Database (ODSI-DB), is a larger dataset containing 316 different oral and dental hyperspectral images.…”

Section: Source Datasetsmentioning

confidence: 99%

Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction

Li,

Ebner,

Noonan

et al. 2021

Preprint

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Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a real-time hyperspectral imaging system for surgical decision-making possible. However, optimal exploitation of the captured data requires solving an ill-posed demosaicking problem and applying additional spectral corrections to recover spatial and spectral information of the image. In this work, we propose a deep learning-based image demosaicking algorithm for snapshot hyperspectral images using supervised learning methods. Due to the lack of publicly available medical images acquired with snapshot mosaic cameras, a synthetic image generation approach is proposed to simulate snapshot images from existing medical image datasets captured by high-resolution, but slow, hyperspectral imaging devices. Image reconstruction is achieved using convolutional neural networks for hyperspectral image super-resolution, followed by cross-talk and leakage correction using a sensor-specific calibration matrix. The resulting demosaicked images are evaluated both quantitatively and qualitatively, showing clear improvements in image quality compared to a baseline demosaicking method using linear interpolation. Moreover, the fast processing time of 45 ms of our algorithm to obtain super-resolved RGB or oxygenation saturation maps per image frame for a state-of-the-art snapshot mosaic camera demonstrates the potential for its seamless integration into real-time surgical hyperspectral imaging applications.

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Oral and Dental Spectral Image Database—ODSI-DB

Cited by 19 publications

References 22 publications

Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions

Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction

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