Photodynamic therapy (PDT) uses photosensitizers (PS) that are excited with light to generate ROS in the presence of oxygen for treating various diseases. PS also has the potential use as photodynamic insecticides (PDI) and for light-inactivation of <i>Leishmania</i> for photodynamic vaccination (PDV). PDT-inactivated <i>Leishmania</i> are non-viable, but remain immunologically competent as whole-cell vaccines against leishmaniasis, and as a universal carrier for delivery of add-on vaccines against other infectious and malignant diseases. We have screened novel PS, including Zn- and Si-phthalocyanines (PC) for differential PDT activities against <i>Leishmania</i>, insect and mammalian cells <i>in vitro</i> to assess their PDI and PDV potential. Here, Zn-PC were conjugated with various functional groups. The conjugates were examined for uptake by cells as a prerequisite for their susceptibility to light-inactivation. PDT sensitivity was found to vary with cell types and PS used. PDI potential of several PS was demonstrated by their mosquito larvicidal PDT activities <i>in vitro</i>. PDT-inactivated <i>Leishmania</i> were stored frozen for PDV in several ongoing studies:  Open label trial with 20 sick dogs for immunotherapy of canine leishmaniasis after chemotherapy in Naples, Italy. Clinical follow-up for >3 years indicate that the PDV prolongs their survival;  PDV of murine models with a human lung cancer vaccine showed dramatic tumor suppression;  Open label trial of multiple PDV via compassionate access to 4 advanced cancer patients showed no clinically adverse effects. Two subjects remain alive. Genetic modifications of <i>Leishmania</i> are underway to further enhance their safety and efficacy for PDV by installation of activable mechanisms for self-destruction and spontaneous light-emission.
In the visualization of three-dimensional (3D) images, specific isosurfaces are usually extracted from 3D images and used to represent (approximate) boundary surfaces of certain structures within 3D images. In order to well approximate the boundary surfaces of these structures, it is important to determine a good isosurface for each boundary surface. An isosurface is said to be a good isosurface of a boundary surface if it can approximate the boundary surface with the smallest error under certain error measuring criteria. The mathematical model describing the approximation problem of a boundary surface by isosurfaces is constructed and studied. The method used to deduce good isosurfaces for the boundary surfaces within 3D discrete images is presented. The proposed method is illustrated by examples with different real 3D biomedical images.
With the increasing popularity of digital camera, organizing and managing the large collection of digital photos effectively are therefore required. In this paper, we study the photo album sorting,
clustering and compression techniques in DCT frequency domain without having to decompress JPEG photos into spatial domain firstly. We utilize the first several non-zero DCT coefficients to build our feature set and calculate the energy histograms in frequency domain directly. We then calculate the similarity distance of every two photos, and perform photo album sorting and adaptive clustering algorithms to group the most similar photos together. We further compress those clustered photos by a MPEG-like algorithm with variable IBP frames and adaptive search windows. Our methods provide a compact and reasonable format for people to store and transmit their large number of digital photos. Experiments prove that our algorithm is efficient and effective for digital photo processing.
During the visualization of volume data, changing the illumination
condition provides us a way to reveal and emphasize the local structures within the volume. However, volume rendering with real-time lighting control is hard. It requires the re-computation of the amount of light received at each voxel after the attenuation, whenever the user changes the lighting condition. In this paper, we describe an image-based approach to relight (change the illumination) the volume in real time. The nature of image-based rendering decouples the rendering time complexity from the resolution of volume data. Hence, real-time relighting of volumetric data is possible even shadow (attenuation) is taken into account. Instead of re-computing all the lighting information, we pre-render (sample) a set of images (reference images) of the volumetric data under different illumination conditions. With these reference images, we are able to relight the volume under desired lighting condition by interpolating and superimposing pixel values. The relighting can be performed on ordinary PCs.
In medical visualization, multiple isosurfaces are usually extracted from medical volume image and used to represent (approximate) the boundary surfaces of different structures in the image. In this paper, we will discuss the approximating problem of the boundary surface (contained within volume image) by isosurface. It is quite common that a medical volume image can contain multiple interesting structures; we present a novel approach for the selection of multiple isosurfaces to approximate the boundary surfaces of these multiple structures. With this approach, the discrete sampling points of the gray values of the boundary surfaces within volume image are computed first. Then by identifying appropriate clusters from the discrete sampling points and computing the mean of each cluster, we can determine the corresponding isosurfaces for approximating these multiple boundary surfaces.
Detection and extraction of the boundary surfaces of interested regions within the medical volume image are important research topics. In this paper, we will introduce several methods to detect, extract and approximate the boundary surfaces within volume image.
Within 3D image, edge surfaces usually correspond to structural boundaries. Therefore, their recognition and modeling are of basic importance in 3D image analysis. Several approaches have been proposed to find or visualize them, such as 3D edge detection and volume rendering algorithms. But edge detectors mainly seek discrete 3D edge-like points in a 3D image, and volume rendering is mainly used for display. Neither extracts a continuous edge surface model, which is usually needed for further analysis, understanding and interpretation of structures within a 3D image. We present two ways, simple and easy to implement, to extract surface models of step-like edge surfaces directly from a 3D image.
In the real world, a doctor can use a knife to cut along any path on the body, unveil the skin and investigate the internals. It would be ideal if the same thing can be done on the volume data with a virtual knife. With this metaphor in mid, we develop a freehand volume cutting tool that allows the doctor to cut the volume in freehand. The cutting path on a volumetric data surface is created with the help of Intelligent Scissor, which is an interactive technique for 2D image segmentation. Our proposed segmentation tool for volume data tends to place the curve/path along the feature lines/curves, hence freeing the doctor from fine tuning the cutting path. Once a closed cutting path is established on the extracted 3D surface volumes along the cutting path. Since the internal cutting surface can be any arbitrary surface, we use a cost minimization technique to make the surface as smooth as possible. Once the volume is partitioned, we can display the cut volumes using a 3D- texture based volume rendering algorithm.
We describe an isosurface extraction algorithm which can generate low resolution isosurfaces, with 4 to 25 times fewer triangles than that generated by marching cubes algorithm, in comparable running times. The key idea is to partition the volume in to variable-sized rectangular boxes and extract isosurface for each box. The flexibility of forming rectangular boxes instead of square boxes improves the triangle reduction ratio. It is faster than postprocessing triangle reduction algorithms to generate low resolution mesh though the triangles in the mesh may not be optimally reduced. The generated mesh also preserve the geometry details of the true isosurface. By climbing from vertices to edges to faces, the algorithm constructs boxes which adapt to the geometry of the true isosurface. Unlike previous adaptive cubes algorithms, the algorithm does not suffer from the gap filling problem.