An automatic lesion detection method for dental x-ray images by segmentation using variational level set

Lin, Phen-Lan; Huang, Po-Ying; Huang, Pengfei

doi:10.1109/icmlc.2012.6359652

Cited by 22 publications

(10 citation statements)

References 11 publications

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“…We use eight periodontitis radiographs provided by Chung Shan Medical University Hospital, Taiwan, to test the performance of our proposed bone-loss detection method and compare the performance with the method using only jBm-H or intensity and a method based on level set in [4] both visually and quantitatively. Figs.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…Finally, by auto-thresholding, we segment the images into normal and bone-loss regions. The experimental results on eight periodontitis radiograph images demonstrated that the proposed method using hybrid of jBm-H and intensity is superior to the method based on a level set segmentation method presented in [4] and the method using only the texture jBm-H or only the intensity in terms of average TPVF and FPVF.…”

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confidence: 79%

“…Comparison of the detected bone-loss areas using the method ABL-Iffim, ABL-ffiM, ABL-I, and the method in[4] …”

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confidence: 99%

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Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

Huang

Lin

et al. 2014

2014 International Conference on Machine Learning and Cybernetics

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Automatic detection of alveolar bone-loss areas in dental periapical radiographs is a very challenging task because of the common uneven illumination problem of dental radiographs and complex topology of bone-loss areas. In this paper, we propose an effective automatic detection method ABL-Imm, which uses weighted average of both the intensity and the texture measured by the H-value of fractal Brownian motions (mm) model. The weights are trained with receiver operating characteristics (ROC) curve based on leave-one-out cross validation mechanism and the principle of the minimum area under the ROC curve (AUC). Through the weighted average of both features, radiograph images are transformed into feature images with the histogram near bimodal distribution. Finally, feature images are segmented into normal and bone-loss regionsby Otsu's auto-thresholding. We test on eight periodontitis radiograph images using the proposed ABL-Imm, the methods with only the feature of mm-H or the intensity, and a method based on level set segmentation, respectively. Experimental results showed that among all the test methods, our proposed ABL-Imm has the highest average TPVF and the lowest average FPVF, when compared with the ground truth (GT) provided by dentists.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

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confidence: 79%

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Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

Huang

Lin

et al. 2014

2014 International Conference on Machine Learning and Cybernetics

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“…and the proposed method uses the approximations of (9) to help deriving the morphological approximation of the region competition term in (3). Fundamentally, when (10) |∇ϕ|( − ) < | |( − ) at belongs to the exterior of the contour.…”

Section: A Morphological Active Contour Formulationmentioning

confidence: 99%

A Morphological Multiphase Active Contour for Vascular Segmentation

Fox¹,

Milanova²,

Al-Ali³

2013

IJBB

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This paper presents a morphological active contour ideal for vascular segmentation in biomedical images. The unenhanced images of vessels and background are successfully segmented using a two-step morphological active contour based upon Chan and Vese's Active Contour without Edges. Using dilation and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust alternative to solving partial differential equations used by traditional level-set Active Contour models. The proposed method is demonstrated with segmented data set images and compared to results garnered from multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.

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“…Often, these partitions are challenging to construct due to noise, low contrast, and image artefacts embedded in the figure. Methods for biomedical segmentation range from basic thresholding techniques [1],fuzzy logic approaches [2], to intricate partial differential equation models [3].…”

Section: Introductionmentioning

confidence: 99%

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2013

IJBB

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An automatic lesion detection method for dental x-ray images by segmentation using variational level set

Cited by 22 publications

References 11 publications

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

Alveolar bone-loss area detection in periodontitis radiographs using hybrid of intensity and texture analyzed based on FBM model

A Morphological Multiphase Active Contour for Vascular Segmentation

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