Automatic clinical image segmentation using pathological modeling, PCA and SVM

Li, Shuo; Fevens, Thomas; Krzyak, Adam; Li, Shangyuan

doi:10.1016/j.engappai.2006.01.011

Cited by 46 publications

(24 citation statements)

References 17 publications

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“…Successful applications have been reported in various industrial processes as well as in other contexts, including the start-up operation in a steel casting process (Zhang and Dudzic, 2006), the operation of a copper smelter (Ja¨msa¨-Jounela et al, 2003), monitoring product quality in the food processing industry (Yu et al, 2003;Sheridan et al, 2006), monitoring of combustion processes (Yu and MacGregor, 2004), remote sensing image analysis (Villmann et al, 2003), fault diagnosis in pre-engaged starter motors (Bay and Bayir, 2005), discovering operational strategies in the refinery fluid catalytic cracking process (Sebzalli and Wang, 2001), medical image analysis (Li et al, 2006), genomics data modelling (Eriksson et al, 2004), increase pharmaceutical data process understanding (Jørgensen et al, 2004), non-destructive testing in pipelines (Cau et al, 2006), adaptive modelling of an offset lithographic printing process (Englund and Verikas, 2007), dynamic modelling of the maize drying process (Liu et al, 2006), and development and comparison of batch processes monitoring strategies (Camacho and Pico´, 2006;Chiang et al, 2006) to mention a variety of examples. This wide variety of examples demonstrates that considerable effort has been placed on applying these multivariate tools for making the most of the operational data available.…”

Section: Introductionmentioning

confidence: 99%

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

Aguado¹,

Montoya²,

Borrás³

et al. 2008

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

Aguado¹,

Montoya²,

Borrás³

et al. 2008

Engineering Applications of Artificial Intelligence

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“…In [13], a variational level set segmentation technique for computer aided dental X-rays analysis was proposed. In [14], variational level set was utilized to detect areas of lesions from periapical dental X-rays. In [15], a hybrid technique based on pathological modeling, PCA and SVM was presented for teeth segmentation.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

et al. 2008

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Purpose Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. Methods In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed by estimating the arc of the upper and lower jaws and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of M. Hosntalab (B) · A. Abbaspour Tehrani-Fard teeth are performed by employing the horizontal and vertical projections of the panoramic dataset, respectively. 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 contours. Results The proposed algorithm was evaluated in the presence of 30 multi-slice CT datasets including 3,600 images. Experimental results reveal the effectiveness of the proposed method. Conclusion In the proposed algorithm, the variational level set technique was utilized to trace the contour of the teeth. In view of the fact that, this technique is based on the characteristic of the overall region of the teeth image, it is possible to extract a very smooth and accurate tooth contour using this technique. In the presence of the available datasets, the proposed technique was successful in teeth segmentation compared to previous techniques.

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“…Li, et al [22] proposed the use of Variational Level Set Method in conjunction with SVM for medical image segmentation. The Variational Level Set Method is used in the feature extraction pipeline to remove highly uncertain regions.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Level Set Method for Image Segmentation

Kristiadi

Pranowo

2017

J. ICT Res. Appl.

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Abstract. Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible -i.e. capturing the whole image -makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-theart performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively.

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Automatic clinical image segmentation using pathological modeling, PCA and SVM

Cited by 46 publications

References 17 publications

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

Deep Convolutional Level Set Method for Image Segmentation

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