We present a semantic based search tool, VIPs, i.e. Visual Image Persons Search, on the domain of VIPs, i.e. very important people. Our tool explores the possibilities of content based image search supported by ontological reasoning. Our framework integrates information from both image processing algorithms and semantic knowledge bases to perform interesting queries that would otherwise be impossible. We describe a novel property reasoner that is able to translate low level image features into semantically relevant object properties. Finally, we demonstrate interesting searches supported by our framework on the domain of people, the majority of whom are movie celebrities, using the properties translated by our system as well as existing ontologies available on the web.

In clinical decision processes, relevant scientific publications and their associated medical images can provide valuable and insightful information. However, effectively searching through both text and image data is a difficult and arduous task. More specifically in the area of image search, finding similar images (or regions within images) poses another significant hurdle for effective knowledge dissemination. Thus, we propose a method using local regions within images to perform and refine medical image retrieval. In our first example, we define and extract large, characteristic regions within an image, and then show how to use these regions to match a query image to similar content. In our second example, we enable the formulation of a mixed query based upon text, image, and region information, to better represent the end user's search intentions. Given our new framework for region-based queries, we present an improved set of similar search results.

We present a novel method for 3D brain tumor volume segmentation based on a parallel cellular automata framework. Our method incorporates prior label knowledge gathered from user seed information to influence the cellular automata decision rules. Our proposed method is able to segment brain tumor volumes quickly and accurately using any number of label classifications. Exploiting the inherent parallelism of our algorithm, we adopt this method to the Graphics Processing Unit (GPU). Additionally, we introduce the concept of individual label strength maps to visualize the improvements of our method. As we demonstrate in our quantitative and qualitative results, the key benefits of our system are accuracy, robustness to complex structures, and speed. We compute segmentations nearly 45x faster than conventional CPU methods, enabling user feedback at interactive rates.

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the semantic gap, a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible layer that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

The accurate and automatic segmentation of tissue regions in cervigram images can aid in the identification and classification of precancerous regions. We implement and analyze four GPU (Graphics Processing Unit) based clustering algorithms: K-means, mean shift, deterministic annealing, and spatially coherent deterministic annealing. From our results, we propose a novel parallel algorithm using the CUDA programming language for digital cervigram segmentation and clustering. The first step of our fully automatic method is to compute the number of modes in the feature space of a color cervigram image using the mean shift clustering algorithm. Next, we use the number of modes in a novel spatially coherent deterministic annealing optimization technique to produce an approximate optimal solution for the clustering problem. Our GPU based methods perform approximately 38x (deterministic annealing), 134x (mean shift), and 276x (spatially coherent deterministic annealing) faster than an equivalent CPU solution. Our implementation decreases the computational time of an annealing method on a 1280x872 pixel image from 5 hours 3 minutes to 72.12 seconds, enabling the use of this optimization method in clinical settings and on large cervigram datasets.