Automated Nasopharyngeal Carcinoma Detection with Dynamic Gadolinium-Enhanced MR Imaging

Hsu, Cheng Chung; Lai, Ping-Hong; Lee, C.; Huang, Wen‐Chen

doi:10.1055/s-0038-1634429

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Studies reported on computer segmentation of NPC are scarce, which may be partly due to the low incidence of NPC in most countries. Hsu et al developed an automatic medical diagnosis for segmenting NPC with dynamic MRI and pharmacokinetic analysis [4]. This method can identify the NPC regions effectively.…”

Section: Introductionmentioning

confidence: 99%

Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine

Zhou

Chan

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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A two-class support vector machine (SVM)-based image segmentation approach has been developed for the extraction of nasopharyngeal carcinoma (NPC) lesion from magnetic resonance (MR) images. By exploring two-class SVM, the developed method can learn the actual distribution of image data without prior knowledge and draw an optimal hyperplane for class separation, via an SVM parameters training procedure and an implicit kernel mapping. After learning, segmentation task is performed by the trained SVM classifier. The proposed technique is evaluated by 39 MR images with NPC and the results suggest that the proposed query-based approach provides an effective method for NPC extraction from MR images with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine

Zhou

Chan

et al.

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.

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“…Huang et al (1999) applied a relative signal increase curve to identify recurrent tumors. Hsu et al (2001) used pharmacokinetic analysis to identify the NPC region. Zhou et al (2003) presented a fuzzy clustering method to filter out normal tissue regions in T1-weighted (T1W) and contrast-enhanced T1W images.…”

Section: Introductionmentioning

confidence: 99%

A hybrid supervised learning nasal tumor discrimination system for DMRI

Huang

Liu

2012

Journal of the Chinese Institute of Engineers

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Dynamic magnetic resonance imaging (DMRI) has become one of the major tools for diagnosing nasal tumors in recent years. The purpose of this research is to develop a system that can discriminate between and enhance the differences between the tumor region and healthy tissue in DMRI automatically during the testing phase. Three supervised learning methods, the Adaboost, support vector machines (SVM), and Bayesian classifiers, are used for discriminating the tumor tissue from the normal tissue. A hybrid method, called ASB, is used to detect whether a given section of tissue is normal or tumor tissue based on the results obtained from the above three classifiers. The first stage of the method is a training process in which a tumor region is manually defined as the ground truth. Subsequently, a testing process is implemented with three typical classifiers, where the results of two out of the three classifiers are applied as a criterion for the discrimination. The experimental results show that ASB has a better differentiating rate than the Adaboost, SVM, and Bayesian classifiers. The ASB system shows an average sensitivity of 98.7%, an average specificity of 93.47%, and an average accuracy of 95.89%.

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“…Studies reported on computer-aided segmentation of NPC from MRI slices are scarce. Among several existing studies, Hsu et al [7] developed an automatic medical diagnosis approach for segmenting NPC with dynamic MRI and pharmacogenetic analysis. This method is only capable of identifying NPC, which makes it a type of "detection" rather than "segmentation" method.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Nasopharyngeal Carcinoma Lesion Segmentation from MRI Using Clustering- and Classification-Based Methods with Learning

2012

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In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-aided tumor segmentation methods (either contour-based or region-based) are necessary to alleviate clinicians' workload. For contour-based methods, a minimal user interaction to draw an initial contour inside or outside the tumor lesion for further curve evolution to match the tumor boundary is preferred, but parameters within most of these methods require manual adjustment, which is technically burdensome for clinicians without specific knowledge. Therefore, segmentation methods with a minimal user interaction as well as automatic parameters adjustment are often favored in clinical practice. In this paper, two region-based methods with parameters learning are introduced for NPC segmentation. Two hundred fifty-three MRI slices containing NPC lesion are utilized for evaluating the performance of the two methods, as well as being compared with other similar region-based tumor segmentation methods. Experimental results demonstrate the superiority of adopting learning in the two introduced methods. Also, they achieve comparable segmentation performance from a statistical point of view.

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Automated Nasopharyngeal Carcinoma Detection with Dynamic Gadolinium-Enhanced MR Imaging

Cited by 5 publications

References 11 publications

Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine

Nasopharyngeal Carcinoma Lesion Segmentation from MR Images by Support Vector Machine

A hybrid supervised learning nasal tumor discrimination system for DMRI

Region-Based Nasopharyngeal Carcinoma Lesion Segmentation from MRI Using Clustering- and Classification-Based Methods with Learning

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