Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

[Breast tumor classification based on serial grayscale ultrasound images].

This paper proposes an algorithm of evaluating the compression depth, and then to extract four normalized mammary elasticity characteristic parameters with respect to the compression depth. The classification experiments show that these elasticity parameters have a good capability in determining whether the tumor is benign or malignant, and if combined with morphological parameters, the accuracy, sensitivity and specificity can be improved and increased to 95.19%, 98.82% and 92.16%, respectively.

[Diagnosis of prostate cancer using SVM-based ultrasound images].

This paper presents a computer-aided diagnosis method for prostate cancer detection using Trans-rectal ultrasound(TRUS) images. Firstly, statistical texture analysis is implemented in every ROI in segmented prostate images. From each ROI, grey level difference vector features, edge-frequency features and texture features in frequency domain are constructed. Then, the number of features is reduced using ANOVA statistics to select the optimal feature subset. Finally, SVM is applied to the selected subset for detecting the cancer regions. Experimental results show that the proposed algorithm can recognize and detect the cancer images effectively so as to supply essential information for a diagnosis.

[Segmentation of the prostate ultrasound image based on an improved geodesic active contour model].

In this paper, we propose that, the need of the costly re-initialization procedure can be completely eliminated by using the variation formulation, thus increasing the speed of computing operations. The edge detecting function in the geodesic active contour model is improved by incorporating a prior knowledge. The accuracy of the segmentation algorithm can be enhanced by using the minimal variance. Experimental results show that the proposed algorithm can segment the prostate ultrasound images effectively and avoid the problem of contour leakage.