Method for Automated Selection of the Trabecular Area in Digital Periapical Radiographic Images Using Morphological Operations

Sela, Enny Itje; Pulungan, Reza; Widyaningrum, Rini; Shantiningsih, Rurie Ratna

doi:10.4258/hir.2019.25.3.193

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The initial stage of the study involves the acquisition of ROI images from periapical radiographs. In this work, ROI is obtained by our software that has been developed using MATLAB, as described in a prior study [ 26 ]. The process of determining ROI begins with selecting a starting point in the trabecular area visible on periapical radiographs, which is taken a minimum 2 mm from the apical of the teeth and determined by avoiding tooth roots, periapical or intraosseous lesions, or variations that affect normal trabecular conditions.…”

Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

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Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

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Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

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“…For this reason, research on osteoporosis examination using dental periapical radiograph images can continue to be carried out, considering that research for osteoporosis examination using dental periapical radiograph images is still rarely used compared to dental panoramic images. This study can be developed by adding a process to increase the resolution of dental periapical radiographs that tend to be a low resolution at the pre-processing stage and applying the automatic ROI selection method [29].…”

Section: Future Workmentioning

confidence: 99%

Periapical Radiograph Texture Features for Osteoporosis Detection using Deep Convolutional Neural Network

Hidjah¹,

Harjoko²,

Wibowo³

et al. 2022

IJACSA

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Currently, research for osteoporosis examination using dental radiographic images is increasing rapidly. Many researchers have used various methods from subject data. It indicates that osteoporosis has become a widespread disease that should be studied more deeply. This study proposes a deep Convolutional Neural Network architecture as a texture feature of dental periapical radiograph for osteoporosis detection. The subject of this study is postmenopausal Javanese women aged over 40 and data measurement result of Bone Mineral Density. The proposed model is divided into stages: 1) stage image acquisition and RoI selection, 2) stage feature extraction and classification. Various experiments with the number of convolution layers (3 layers to 6 layers) and various input block sizes and other hyper parameters were used to get the best model. The best model is obtained when the input image size is greater than 100 and less than 150 and a five of convolution layer, as well as other hyper parameters, including epochs=100, dropout=0.5, learning rate=0.0001, batch size= 16 and loss function using Adam's optimization. Validation and testing accuracy achieved by the best model is 98.10%, and 92.50. The research shows that the bigger images provide additional information about trabecular patterns in normal, osteopenia and osteoporosis classes, so that the proposed method using deep convolutional neural network as textural feature of the periapical radiograph achieves a good performance for detection osteoporosis.

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“…Currently, computer vision is widely applied in various fields, including medicine, to diagnose and analyze diseases based on medical image. The disorders diagnosed using medical images include thyroid cancer [ 1 ] on ultrasound images, back pain [ 2 ] on computed tomography scans, breast cancer [ 3 ] on mammograms, dental and oral diseases [ 4 ] on radiographic images, abnormalities of spinal intervertebral discs [ 5 , 6 ] on magnetic resonance image, and retinal diseases—diabetes mellitus [ 7 , 8 ] and glaucoma [ 9 , 10 ]—on fundus images. Glaucoma is an eye disease that may be the second-largest cause of blindness in the world.…”

Section: Introductionmentioning

confidence: 99%

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

et al. 2023

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Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN).Methods: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data.Results: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset.Conclusions: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.

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Method for Automated Selection of the Trabecular Area in Digital Periapical Radiographic Images Using Morphological Operations

Cited by 6 publications

References 14 publications

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Periapical Radiograph Texture Features for Osteoporosis Detection using Deep Convolutional Neural Network

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

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