Semi-automatic computer aided lesion detection in dental X-rays using variational level set

Li, Shuo; Fevens, Thomas; Krzyak, Adam; Jin, Chao; Song, Li

doi:10.1016/j.patcog.2007.01.012

Cited by 66 publications

(29 citation statements)

References 20 publications

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“…total number of teeth being correctly isolated total number of teeth *100%, (11) in which "teeth being correctly isolated" means that every segmented ROI contains either a single tooth where teeth exist or no tooth where a missing tooth is detected. Table 1 lists the total number of teeth being correctly isolated and the accuracy of both methods for the upper and lower jaws, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Accurate teeth− −shape and −size can only be obtained through accurate teeth segmentation, teeth segmentation is thus an essential step for achieving high accuracy of the subsequent stages of ADIS. Meanwhile, in a computer−aided dental diagnosis system (CADDS), such as caries detection in panoramic dental X−ray images [10] or lesion detection in dental X−ray images [11], caries and lesions are detected after teeth seg− mentation. Thus, teeth segmentation is also an essential step for achieving high accuracy of caries anf lesion detection.…”

Section: Introductionmentioning

confidence: 99%

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An automatic and effective tooth isolation method for dental radiographs

Lin

Huang

Cho

et al. 2013

Opto-Electronics Review

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Tooth isolation is a very important step for both computer-aided dental diagnosis and automatic dental identification systems, because it will directly affect the accuracy of feature extraction and, thereby, the final results of both types of systems. This paper presents an effective and fully automatic tooth isolation method for dental X-ray images, which contains up-per-lower jaw separation, single tooth isolation, over-segmentation verification, and under-segmentation detection. The upper-lower jaw separation mechanism is based on a gray-scale integral projection to avoid possible information loss and incorporates with the angle adjustment to handle skewed images. In a single tooth isolation, an adaptive windowing scheme for locating gap valleys is proposed to improve the accuracy. In over-segmentation, an isolation-curve verification scheme is proposed to remove excessive curves; and in under-segmentation, a missing-teeth detection scheme is proposed. The experimental results demonstrate that our method achieves the accuracy rates of 95.63% and 98.71% for the upper and lower jaw images, respectively, from the test database of 60 bitewing dental radiographs, and performs better for images with severe teeth occlusion, excessive dental works, and uneven illumination than that of Nomir and Abdel-Mottaleb’s method. The method without upper-lower jaw separation step also works well for panoramic and periapical images.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An automatic and effective tooth isolation method for dental radiographs

Lin

Huang

Cho

et al. 2013

Opto-Electronics Review

View full text Add to dashboard Cite

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“…After that, some control point manually selected by the specialist if it is necessary. In order to have a mathematics power instrument to manage the line and possibly modify it interactively, we exploit a B-spline representation [17]. Figure 17 (c) shows the final panorex line overlaid over the CT slice.…”

Section: Discussionmentioning

confidence: 99%

“…In [16], [17], a variational level set segmentation technique for computer aided dental X-rays analysis was proposed. In [18], a hybrid technique based on pathological modeling, principal component analysis (PCA) and support vector machine (SVM) was presented for teeth segmentation.…”

Section: Copyright C 2009 the Institute Of Electronics Information Amentioning

confidence: 99%

A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures

Hosntalab

Zoroofi

Tehrani‐Fard

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYTeeth segmentation in computed tomography (CT) images is a major and challenging task for various computer assisted procedures. In this paper, we introduced a hybrid method for quantification of teeth in CT volumetric dataset inspired by our previous experiences and anatomical knowledge of teeth and jaws. In this regard, we propose a novel segmentation technique using an adaptive thresholding, morphological operations, panoramic re-sampling and variational level set algorithm. The proposed method consists of several steps as follows: first, we determine the operation region in CT slices. Second, the bony tissues are separated from other tissues by utilizing an adaptive thresholding technique based on the 3D pulses coupled neural networks (PCNN). Third, teeth tissue is classified from other bony tissues by employing panorex lines and anatomical knowledge of teeth in the jaws. In this case, the panorex lines are estimated using Otsu thresholding and mathematical morphology operators. Then, the proposed method is followed by calculating the orthogonal lines corresponding to panorex lines and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the integral projections of the panoramic dataset. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a variational level set to refine initial teeth boundaries to final contour. In the last step a surface rendering algorithm known as marching cubes (MC) is applied to volumetric visualization. The proposed algorithm was evaluated in the presence of 30 cases. Segmented images were compared with manually outlined contours. We compared the performance of segmentation method using ROC analysis of the thresholding, watershed and our previous works. The proposed method performed best. Also, our algorithm has the advantage of high speed compared to our previous works.

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“…To segment a triple-region image using coupled level set method, three coupled level set functions are usually used. However in [7], Li et al proposed a competitive level set which uses two coupled level set functions to segment triple regions. In general, the coupled level set suffers from the slow converge and local minimal problem.…”

Section: Coupled Versus Hierarchicalmentioning

confidence: 99%

Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function

Fevens

Krzyżak

et al. 2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

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Abstract. This paper proposes a novel method for clinical triple-region image segmentation using a single level set function. Triple-region image segmentation finds wide application in the computer aided X-ray, CT, MRI and ultrasound image analysis and diagnosis. Usually multiple level set functions are used consecutively or simultaneously to segment triple-region medical images. These approaches are either time consuming or suffer from the convergence problems. With the new proposed triple-regions level set energy modelling, the triple-region segmentation is handled within the two region level set framework where only one single level set function needed. Since only a single level set function is used, the segmentation is much faster and more robust than using multiple level set functions. Adapted to the clinical setting, individual principal component analysis and a support vector machine classifier based clinical acceleration scheme are used to accelerate the segmentation. The clinical acceleration scheme takes the strengths of both machine learning and the level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both synthesized and practical images are used to test the proposed method. These results show that the proposed method is able to successfully segment the triple-region using a single level set function. Also this segmentation is very robust to the placement of initial contour. While still quickly converging to the final image, with the clinical acceleration scheme, our proposed method can be used during pre-processing for automatic computer aided diagnosis and surgery.

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Semi-automatic computer aided lesion detection in dental X-rays using variational level set

Cited by 66 publications

References 20 publications

An automatic and effective tooth isolation method for dental radiographs

An automatic and effective tooth isolation method for dental radiographs

A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures

Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function

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