A new way for multidimensional medical data management: volume of interest (VOI)-based retrieval of medical images with visual and functional features.

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.     

A set expression based inheritance system

This paper describes a new formalism for inheritance systems, based on the formal semantics of set expressions. Using the formalism, it is possible to define new semantic classes by arbitrary set expressions operating on previously defined classes. Thus generalizing bothIS-A links andIS-NOT-A links and adding the set intersection operation. We present an efficient algorithm which follows these definitions to deduce the properties implied by the inheritance network, i.e., the properties of the classes containing a given element. The application which motivated the development of the formalism, namely semantic disambiguation of natural language, is also described.     

A technique for extracting physiological parameters and therequired input function simultaneously from PET image measurements:theory and simulation study

Positron emission tomography (PET) is an important tool for enabling quantification of human brain function. However, quantitative studies using tracer kinetic modeling require the measurement of the tracer time-activity curve in plasma (PTAC) as the model input function. It is widely believed that the insertion of arterial lines and the subsequent collection and processing of the biomedical signal sampled from the arterial blood are not compatible with the practice of clinical PET, as it is invasive and exposes personnel to the risks associated with the handling of patient blood and radiation dose. Therefore, it is of interest to develop practical noninvasive measurement techniques for tracer kinetic modeling with PET. In this paper, a technique is proposed to extract the input function together with the physiological parameters from the brain dynamic images alone. The identifiability of this method is tested rigorously by using Monte Carlo simulation. The results show that the proposed method is able to quantify all the required parameters by using the information obtained from two or more regions of interest (ROIs) with very different dynamics in the PET dynamic images. There is no significant improvement in parameter estimation for the local cerebral metabolic rate of glucose (LCMRGlc) if there are more than three ROIs. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study     

Harnessing the Melting Peculiarities of Ultra‐High Molecular Weight Polyethylene Fibers for the Processing of Compacted Fiber Composites

Summary: Compacted fiber composites offer unique properties due to their lack of an extraneous matrix. The conditions of processing ultra‐high molecular weight polyethylene (UHMWPE) fibers were simulated in a heated pressure cell. In situ X‐ray diffraction measurements were used to follow the relevant transitions and the changes in the degree of crystallinity during melting and crystallization. The results strongly support the suggestion that the hexagonal crystal phase, in which the chain conformation is extremely mobile on the segmental level, constitutes the physical basis of compaction technologies for processing UHMWPE fibers into a single‐polymer composite. This report suggests that using a pseudo‐phase diagram outlining the occurrence of different phases during slow heating and the degree of crystallinity can provide valuable insight into the technological parameters relevant for optimal processing conditions.

Degree of crystallinity as a function of pressure and temperature in a region relevant to compaction processes.     

Precipitation from glassy polymer solutions

Solid solutions of general-purpose polystyrene containing crystalline tetrabromoxylene, tetrachloroxylene, or tetrachlorobenzene were prepared. The effects of the low molecular weight crystalline additive concentration and the thermal history on the thermal properties of the system have been studied. The system solubility, rejection of solute, etc., were characterized by such techniques as rheology, thermal analysis (DSC), and Vicat softening point.     

Theory and experiment on the three-dimensional motion of a freely suspended spherical particle at the entrance to a pore at low Reynolds number

This paper examines the three dimensional approach of a neutrally buoyant sphere of any size to a circular hole in a plane wall at very low Reynolds number. The analysis differs from previous studies, largely applied to the multipore filter, in that the authors investigate the three dimensional hydrodynamic interaction of the sphere with the entrance geometry of the pore in the limit of zero inertia. The problem is first exactly formulated as the linear superposition of fundamental Stokes flows for rotation and translation of the sphere and flow past a stationary sphere. Approximate solutions are then obtained for the deviation of sphere trajectories from fluid stream lines and the results compared with laboratory model experiments in a low Reynolds number settling tank. The theory and experiment show that because of the hydrodynamic interaction sphere trajectories and fluid streamlines deviate significantly as the opening is approached, that all spheres eventually enter the pore regardless of their initial position, but that a non-uniform concentration profile develops with large increases in concentration near the walls of the orifice in the vicinity of the opening. The results provide a basic mechanism to explain the onset of Fahraeus phenomenon for red cells entering small blood vessels and glass tubes.     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Distilled Collections from Textual Image Queries

We present a distillation algorithm which operates on a large, unstructured, and noisy collection of internet images returned from an online object query. We introduce the notion of a distilled set, which is a clean, coherent, and structured subset of inlier images. In addition, the object of interest is properly segmented out throughout the distilled set. Our approach is unsupervised, built on a novel clustering scheme, and solves the distillation and object segmentation problems simultaneously. In essence, instead of distilling the collection of images, we distill a collection of loosely cutout foreground “shapes”, which may or may not contain the queried object. Our key observation, which motivated our clustering scheme, is that outlier shapes are expected to be random in nature, whereas, inlier shapes, which do tightly enclose the object of interest, tend to be well supported by similar shapes captured in similar views. We analyze the commonalities among candidate foreground segments, without aiming to analyze their semantics, but simply by clustering similar shapes and considering only the most significant clusters representing non‐trivial shapes. We show that when tuned conservatively, our distillation algorithm is able to extract a near perfect subset of true inliers. Furthermore, we show that our technique scales well in the sense that the precision rate remains high, as the collection grows. We demonstrate the utility of our distillation results with a number of interesting graphics applications.     

Semantic context based refinement for news video annotation

Automatic video annotation is to bridge the semantic gap and facilitate concept based video retrieval by detecting high level concepts from video data. Recently, utilizing context information has emerged as an important direction in such domain. In this paper, we present a novel video annotation refinement approach by utilizing extrinsic semantic context extracted from video subtitles and intrinsic context among candidate annotation concepts. The extrinsic semantic context is formed by identifying a set of key terms from video subtitles. The semantic similarity between those key terms and the candidate annotation concepts is then exploited to refine initial annotation results, while most existing approaches utilize textual information heuristically. Similarity measurements including Google distance and WordNet distance have been investigated for such a refinement purpose, which is different with approaches deriving semantic relationship among concepts from given training datasets. Visualness is also utilized to discriminate individual terms for further refinement. In addition, Random Walk with Restarts (RWR) technique is employed to perform final refinement of the annotation results by exploring the inter-relationship among annotation concepts. Comprehensive experiments on TRECVID 2005 dataset have been conducted to demonstrate the effectiveness of the proposed annotation approach and to investigate the impact of various factors.