Gray-Level Co-occurrence Matrix Bone Fracture Detection

Hum, Yan Chai; Lai, Khin Wee; Swee, Tan Tian; Salleh, Sheikh Hussain Shaikh; Ariff, A. K.; Kamarulafizam, [No Value]

doi:10.3844/ajassp.2011.26.32

Cited by 60 publications

(27 citation statements)

References 10 publications

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“…Both k-means clustering and active contour models ("snakes") have been implemented in other medical image segmentation applications [23][24][25]. However, these techniques have not been used for segmentation tasks involving periodontal high-frequency ultrasound images.…”

Section: Discussionmentioning

confidence: 99%

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

et al. 2015

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The primary aim of this study was to demonstrate that periodontal ultrasonography is a reliable method with which to identify and evaluate the attachment level of the gingival junctional epithelium. A secondary aim was to devise an automated computer-assisted method that allows the examiner to more easily identify the gingival sulcus contour on ultrasound images. Material and methods: This in vitro study was carried out on 36 sites on the lingual surface of eight pig mandibles. For each site, periodontal ultrasonography was performed by the same examiner, using DermaScan C Cortex Technology (Denmark) with a 20-MHz transducer. Subsequently, the mandibles were sectioned with a microtome and examined by direct microscopy. To facilitate identification of the gingival sulcus on ultrasound images, a computational method was adopted. Results: Computer processing of the ultrasound images slightly modified the contour of the gingival sulcus. The absolute mean differences in the linear measurements of the Dermascan-automated computer-generated values and the corresponding values of microscopy, which is the gold standard, varied between 0.06 and 1.75 mm. Statistical analysis showed that with respect to the gingival sulcus height, the correlation between the computer-processed ultrasound images and the direct microscopy images was stronger than the correlation between the non-processed ultrasound images and those from direct microscopy. Conclusions: Ultrasonographic examination of the periodontal tissues allows the examiner to localize the gingival epithelial attachment level and provides substantial data regarding the soft gingival tissues.

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Section: Discussionmentioning

confidence: 99%

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

et al. 2015

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“…Therefore, the usage of four directions enables the proposed method to generate good crack detection result. As we can see from (6), only the Note that the gray level of the image is normalized between 0~1 before applying the filter. Furthermore, for the detection of dark cracks, a gray level inversion operation should be implemented before applying the filter.…”

Section: Output Of the Proposed Filtermentioning

confidence: 99%

“…Moreover, it fails to detect various sizes of crack. H. Y. Chai, et al [6] proposed a method using GLCM (Gray-Level Co-occurrence Matrix) [7] computerized techniques to detect femur bone fracture in X-ray images automatically. Since this method uses GLCM, it is sensitive to image noise.…”

Section: Introductionmentioning

confidence: 99%

A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

Peng¹,

Nam²

2016

Journal of Electrical Engineering and Technology

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-Internal cracks in products are invisible and can lead to fatal crashes or damage. Since Xrays can penetrate materials and be attenuated according to the material's thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

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“…Early methods for texture classification focus on the statistical (Chai et al, 2011), structural, model based and signal processing analysis of texture images (Tuceryan and Jain, 1993). In general their classification results are good as long as the training and test samples have identical or similar orientations.…”

Section: Introductionmentioning

confidence: 99%

Quaternion Photometric Stereo for Rotation Invariant Surface Texture Classification

Sathyabama¹

2011

American Journal of Applied Sciences

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Problem statement:The escalating growth of computer vision applications has increased the need for faster and more accurate image analysis algorithms. One application of image analysis that has been studied for a long time is texture analysis. The majority of existing texture analysis methods makes the explicit or implicit assumption that texture images are acquired from the same viewpoint. This study presents a rotationally invariant descriptor for textures with different orientations based on the Quaternion Representation. Approach: A novel Quaternion Photometric Stereo (QPS) was proposed for Rotation invariant classification of 3D surface textures. QPS was constructed by placing each pixel of three images of same texture with different orientation into the three imaginary parts of the quaternion, leaving the real part zero. The Peak Distribution Norm Vector (PDNV) was extracted from the radial plot of the Quaternion Fourier spectrum as rotation invariant texture signature used for texture classification. Results: The quaternion representation of stereo images was to be effective in the context of Rotation Invariant Texture classification. Conclusion: The proposed Quaternion approach gives a successful classification rate with computational advantages than the previously developed Monochrome and Color Photometric Stereo Methods.

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Gray-Level Co-occurrence Matrix Bone Fracture Detection

Cited by 60 publications

References 10 publications

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

Quaternion Photometric Stereo for Rotation Invariant Surface Texture Classification

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