Extracting the Potential Features of Digital Panoramic Radiograph Images by Combining Radio Morphometry Index, Texture Analysis, and Morphological Features

Sela, Enny Itje; Sutarman, Sutarman

doi:10.3844/jcssp.2018.144.152

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It provides a robust method for face recognition (Eleyan and Irel, 2011). It has been applied to 3D printing (Fastowicz and Okarma, 2016), Digital Panoramic Radiograph Images (Enny and Sutarman, 2017), mammogram analysis (Pratiwi et al, 2015) and audio processing (Lacerda and Mello, 2017). This research takes a new direction, deploying GLCM to capture statistical features of SS signals.…”

Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Direct Sequence and Frequency Hopping Signals Classification Based on Co-Occurrence Matrix and Clustering Techniques

Fouad¹,

Elsayed²,

Girgis³

2019

Journal of Computer Science

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Spread Spectrum techniques (SS) were first developed for military applications, but currently, they have commercial applications. SS provides secure communication and allows multiple accesses for same radio spectrum. So, most Wireless Local Area Network (WLAN) systems use it, as do Cognitive Radios (CR), space systems and Global Positioning Systems (GPS). Direct Sequence Spread Spectrum (DSSS) and frequency hopped spread spectrum (FHSS) are the two most-used techniques today. Nowadays radio spectrum has become very crowded and so now there is a need for spectrum efficiency. Automatic SS classification presents a rather difficult problem, especially if the parameters, such as the signal power, carrier frequency, etc., are unknown. This research takes a new direction; it deploys the Gray Level Co-occurrence Matrix (GLCM) to capture statistical features of SS signals. Using GLCM, 22 features are extracted for each vector of signal. Analyzing the signals is done in the time domain which measures the variation of amplitude of signals with time. Therefore, the main contribution is to apply and show how GLCM improves the identification accuracy of the two signals in presence of noise. The proposed model achieves considerably accurate results even with a low SNR. GLCM features help classifiers to achieve average accuracy 84% and reach 100% signal identification at a zero SNR. To prove the superiority of these features, a variety of clustering methods are applied, such as centroid, connectivity, model-based and message-passing models. Clustering performance results based on GLCM features are compared With Principal Components Analysis (PCA), Kernel-based Principal Components Analysis (KPCA) and fast Independent Components Analysis (Fast-ICA). Clustering results are evaluated with external and internal validity indices. The accuracy was tested over 26 levels of Signal-to-Noise Ratios (SNR).

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Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Direct Sequence and Frequency Hopping Signals Classification Based on Co-Occurrence Matrix and Clustering Techniques

Fouad¹,

Elsayed²,

Girgis³

2019

Journal of Computer Science

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“…One part of the ROI that is often used for identifying diseases by dentists is the trabecular bone, which is located under the root of the tooth (Figure 1). Previous studies have required image enhancement because of the poor quality of ROI images on the trabecular area [141516]. Originally, the selection of ROIs was usually performed by visual observation and cropping using tools, such as Photoshop, Corel Draw, etc.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Objectives The aim of this study is to propose a method that automatically select the trabecular bone area in digital periapical radiographic images using a sequence of morphological operations. Methods The study involved 50 digital periapical radiographic images of women aged from 36 to 58 years old. The proposed method consists of three stages: teeth detection, trabecular identification, and validation. A series of morphological operations—top-hat and bottom-hat filtering, automatic thresholding, closing, labeling, global thresholding, and image subtraction—are performed to automatically obtain the trabecular bone area in images. For validation, the results of the proposed method were compared with those of two dentists pixel by pixel. Three parameters were used in the validation: trabecular area, percentage of agreed area, and percentage of disagreed area. Results The proposed method obtains the trabecular bone area in a polygon. The obtained trabecular bone area is usually larger than that of previous studies, but is usually smaller than the dentists'. On average over all images, the trabecular area produced by the proposed method is 5.83% smaller than that identified by dentists. Furthermore, the average percentage of agreed area and the average percentage of disagreed area of the proposed method against the dentists' results were 75.22% and 8.75%, respectively. Conclusions The shape of the trabecular bone area produced by the proposed method is similar and closer to that identified by dentists. The method, which consists of only simple morphological operations on digital periapical radiographic images, can be considered for selecting the trabecular bone area automatically.

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Osteoporosis identification based on the validated trabecular area on digital dental radiographic images

Sela

Pulungan

2019

Procedia Computer Science

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Extracting the Potential Features of Digital Panoramic Radiograph Images by Combining Radio Morphometry Index, Texture Analysis, and Morphological Features

Cited by 3 publications

References 8 publications

Direct Sequence and Frequency Hopping Signals Classification Based on Co-Occurrence Matrix and Clustering Techniques

Direct Sequence and Frequency Hopping Signals Classification Based on Co-Occurrence Matrix and Clustering Techniques

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

Osteoporosis identification based on the validated trabecular area on digital dental radiographic images

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