Microscopic image segmentation based on pixel classification and dimensionality reduction

Pathological image analysis plays a significant role in effective disease diagnostics. In this article, a tool for diagnosis assistance by automatic segmentation of bone marrow images is introduced. The aim of our segmentation is to demarcate cell's component: nucleus, cytoplasm, red cells, and background. Different color spaces were used to extract color's features to profit of their complementarity. We introduce several dimensionality reduction techniques. These techniques are exemplified on a support vector machine pixel‐based bone marrow image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy and time consuming. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 22–28, 2013     

Why does signaling enhance multimedia learning? Evidence from eye movements

Previous studies have suggested that signaling enhances multimedia learning. However, there is not enough evidence showing why signaling leads to better performance. The goal of this study was to examine the effects of signaling on learning outcomes and to reveal the underlying reasons for this effect by using eye movement measures. The participants were 40 undergraduate students who were presented with either signaled or nonsignaled multimedia materials. Labels in the illustration were signaled by temporarily changing the color of the items. The results suggest that the signaled group outperformed the nonsignaled group on transfer and matching tests. Eye movement data shows that signaling guided attention to relevant information and improved the efficiency and effectiveness of finding necessary information.