Jiayin Zhou

Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine

A novel image segmentation approach by exploring one-class support vector machine (SVM) has been developed for the extraction of brain tumor from magnetic resonance (MR) images. Based on one-class SVM, the proposed method has the ability of learning the nonlinear distribution of the image data without prior knowledge, via the automatic procedure of SVM parameters training and an implicit learning kernel. After the learning process, the segmentation task is performed. The proposed technique is applied to 24 clinical MR images of brain tumor for both visual and quantitative evaluations. Experimental results suggest that the proposed query-based approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy

Segmentation and visualization of nasopharyngeal carcinoma using MRI

In this study, a semi-automatic system was developed for nasopharyngeal carcinoma (NPC) tumor segmentation, volume measurement and visualization using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI and inter-slice interpolation were integrated in this medical diagnosis system. This system was applied to 10 MR image data sets of NPC patients and satisfactory results were achieved. This system can be used as a clinical image analysis tool for doctors or radiologists to obtain tumor location from MRI, tumor volume estimation, and 3D information.

Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine

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

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

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.

Extraction of tongue carcinoma using genetic algorithm-induced fuzzy clustering and artificial neural network from MR images

A novel hierarchical image segmentation approach has been developed for the extraction of tongue carcinoma from magnetic resonance (MR) images. First, a genetic algorithm (GA)-induced fuzzy clustering is used for initial segmentation of MR images of head and neck. Then these segmented masses are refined to reduce the false-positives using an artificial neural network (ANN)-based symmetry detection and image analysis procedure. The proposed technique is applied to clinical MR images of tongue carcinoma and quantitative evaluations are performed. Experimental results suggest that the proposed approach provides an effective method for tongue carcinoma extraction with high accuracy and minimal user-dependency.

Extraction of Metastatic Lymph Nodes from MR Images Using Two Deformable Model-based Approaches

We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node–normal tissue interface. Differently, the level sets move a manually drawn initial contour toward the desired nodal boundary under the control of the evolvement speed function, which is influenced by image gradient force. The two methods were tested by extracting 33 metastatic cervical nodes from 18 nasopharyngeal carcinoma patients. Experiments on a basis of pixel matching to reference standard showed that RPCD and level sets achieved averaged percentage matching at 82–84% and 87–88%, respectively. In addition, both methods had significantly lower interoperator variances than the manual tracing method. It was suggested these two methods could be useful tools for the evaluation of metastatic nodal volume as an indicator of classification and treatment response, or be alternatives for the delineation of metastatic nodal lesions in radiation treatment planning.

Tumor volume measurement for nasopharyngeal carcinoma using knowledge-based fuzzy clustering MRI segmentation

A knowledge-based fuzzy clustering (KBFC) MRI segmentation algorithm was proposed to obtain accurate tumor segmentation for tumor volume measurement of nasopharyngeal carcinoma (NPC). An initial segmentation was performed on T1 and contrast enhanced T1 MR images using a semi-supervised fuzzy c-means (SFCM) algorithm. Then, three types of anatomic and space knowledge--symmetry, connectivity and cluster center were used for image analysis which contributed the final tumor segmentation. After the segmentation, tumor volume was obtained by multi-planimetry method. Visual and quantitative validations were performed on phantom model and six data volumes of NPC patients, compared with ground truth (GT) and the results acquired using seeds growing (SG) for tumor segmentation. In visual format, KBFC showed better tumor segmentation image than SG. In quantitative segmentation quality estimation, on phantom model, the matching percent (MP) / correspondence ratio (CR) was 94.1-96.4% / 0.888-0.925 for KBFC and 94.1-96.0% / 0.884-0.918 for SG while on patient data volumes, it was 92.1+/- 2.6% / 0.884+/- 0.014 for KBFC and 87.4+/- 4.3% / 0.843+/- 0.041 for SG. In tumor volume measurement, on phantom model, measurement error was 4.2-5.0% for KBFC and 4.8-6.1% for SG while on patient data volumes, it was 6.6+/- 3.5% for KBFC and 8.8+/- 5.4% for SG. Based on these results, KBFC could provide high quality of MRI tumor segmentation for tumor volume measurement of NPC.