A picture is worth a thousand words. Biomedical researchers tend to incorporate a significant number of images (i.e., figures or tables) in their publications to report experimental results, to present research models, and to display examples of biomedical objects. Unfortunately, this wealth of information remains virtually inaccessible without automatic systems to organize these images. We explored supervised machine-learning systems using Support Vector Machines to automatically classify images into six representative categories based on text, image, and the fusion of both. Our experiments show a significant improvement in the average Fscore of the fusion classifier (73.66%) as compared with classifiers just based on image (50.74%) or text features (68.54%).

In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.

Background: Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.

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This work reports electrothermal MEMS parallel plate-rotation (PPR) for a single-imager based stereoscopic endoscope. A thin optical plate was directly connected to an electrothermal MEMS microactuator with bimorph structures of thin silicon and aluminum layers. The fabricated MEMS PPR device precisely rotates an transparent optical plate up to 37° prior to an endoscopic camera and creates the binocular disparities, comparable to those from binocular cameras with a baseline distance over 100 μm. The anaglyph 3D images and disparity maps were successfully achieved by extracting the local binocular disparities from two optical images captured at the relative positions. The physical volume of MEMS PPR is well fit in 3.4 mm x 3.3 mm x 1 mm. This method provides a new direction for compact stereoscopic 3D endoscopic imaging systems.