Enhanced visualization of histological samples with an adjustable RGB contrast system with application for tissue used in photodynamic therapy

The analysis of histological sections has long been a valuable tool in the pathological studies. The interpretation of tissue conditions, however, relies directly on visual evaluation of tissue slides, which may be difficult to interpret because of poor contrast or poor color differentiation. The Chromatic Contrast Visualization System (CCV) combines an optical microscope with electronically controlled light-emitting diodes (LEDs) in order to generate adjustable intensities of RGB channels for sample illumination. While most image enhancement techniques rely on software post-processing of an image acquired under standard illumination conditions, CCV produces real-time variations in the color composition of the light source itself. The possibility of covering the entire RGB chromatic range, combined with the optical properties of the different tissues, allows for a substantial enhancement in image details. Traditional image acquisition methods do not exploit these visual enhancements which results in poorer visual distinction among tissue structures. Photodynamic therapy (PDT) procedures are of increasing interest in the treatment of several forms of cancer. This study uses histological slides of rat liver samples that were induced to necrosis after being exposed to PDT. Results show that visualization of tissue structures could be improved by changing colors and intensities of the microscope light source. PDT-necrosed tissue samples are better differentiated when illuminated with different color wavelengths, leading to an improved differentiation of cells in the necrosis area. Due to the potential benefits it can bring to interpretation and diagnosis, further research in this field could make CCV an attractive technique for medical applications.

Further probing the nature of FSR 1767

With Two-Micron All-Sky Survey (2MASS) photometry and proper motions, Bonatto et al. suggested that FSR 1767 is a globular cluster (GC), while with J and K NTT/SOFI photometry Froebrich, Meusinger & Scholz concluded that it is not a star cluster. In this study, we combine previous and new evidence that are consistent with a GC. For instance, we show that the horizontal branch (HB) and red giant branch (RGB) stars, besides sharing a common proper motion, have radial density profiles that consistently follow the King`s law independently. Reddening maps around FSR 1767 are built using the bulge RGB as reference and also Schlegel`s extinction values to study local absorptions. Both approaches provide similar maps and show that FSR 1767 is not located in a dust window, which otherwise might have produced the stellar overdensity. Besides, neighbouring regions of similar reddening as FSR 1767 do not present the blue HB stars that are a conspicuous feature in the colour-magnitude diagram of FSR 1767. We report the presence of a compact group of stars located in the central parts of FSR 1767. It appears to be a detached post-collapse core, similar to those of other nearby low-luminosity GCs projected towards the bulge. We note that while the NTT/SOFI photometry of the star cluster FSR 1716 matches perfectly that from 2MASS, it shows a considerable offset for FSR 1767. We discuss the possible reasons why both photometries differ. We confirm our previous structural and photometric fundamental parameters for FSR 1767, which are consistent with a GC.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.