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.

Monitoring blood flow rate inside prosthetic vascular grafts enables an early detection of the graft degradation, followed by the timely intervention and prevention of the graft failure. This paper presents an inductively powered implantable blood flow sensor microsystem with bidirectional telemetry. The microsystem integrates silicon nanowire (SiNW) sensors with tunable piezoresistivity, an ultralow-power application-specific integrated circuit (ASIC), and two miniature coils that are coupled with a larger coil in an external monitoring unit to form a passive wireless link. Operating at 13.56-MHz carrier frequency, the implantable microsystem receives power and command from the external unit and backscatters digitized sensor readout through the coupling coils. The ASIC fabricated in 0.18-μm CMOS process occupies an active area of 1.5 × 1.78 mm (2) and consumes 21.6 μW only. The sensors based on the SiNW and diaphragm structure provide a gauge factor higher than 300 when a small negative tuning voltage (-0.5-0 V) is applied. The measured performance of the pressure sensor and ASIC has demonstrated 0.176 mmHg/√Hz sensing resolution.

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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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Recent findings show that tumor volume is a significant prognostic factor for the treatment of nasopharyngeal carcinoma (NPC). The inclusion of tumor volume as an additional prognostic factor in the UICC TNM classification system was suggested; however, how tumor volume could possibly be incorporated is still unexplored. In this paper, we report a quantitative analysis on the relationship between NPC tumor volume and T-classification, using the data from 206 NPC patients. By T-classification and semi-automatic tumor volume measurement, the difference in tumor volumes among the various TNM T-classification groups was examined. In addition, a statistics-based analysis scheme, which used the T-classification as the "gold standard", was proposed to classify NPC tumors into volume-based groups to explore the possible links. The results show that NPC tumor volume has positive correlation with advancing T-classification groups and significant difference existed in the distribution of T-classification among various volume-based groups (P < 0.001). By the proposed statistical scheme, tumor volume could be included as an additional prognostic factor in the TNM framework, following validation studies.