Computer-aided detection (CAD) algorithms 'automatically' identify lung nodules on thoracic multi-slice CT scans
(MSCT) thereby providing physicians with a computer-generated 'second opinion'. While CAD systems can achieve
high sensitivity, their limited specificity has hindered clinical acceptance. To overcome this problem, we propose a false
positive reduction (FPR) system based on image processing and machine learning to reduce the number of false positive
lung nodules identified by CAD algorithms and thereby improve system specificity.
To discriminate between true and false nodules, twenty-three 3D features were calculated from each candidate nodule's
volume of interest (VOI). A genetic algorithm (GA) and support vector machine (SVM) were then used to select an
optimal subset of features from this pool of candidate features. Using this feature subset, we trained an SVM classifier to
eliminate as many false positives as possible while retaining all the true nodules. To overcome the imbalanced nature of
typical datasets (significantly more false positives than true positives), an intelligent data selection algorithm was
designed and integrated into the machine learning framework, thus further improving the FPR rate.
Three independent datasets were used to train and validate the system. Using two datasets for training and the third for
validation, we achieved a 59.4% FPR rate while removing one true nodule on the validation datasets. In a second
experiment, 75% of the cases were randomly selected from each of the three datasets and the remaining cases were used
for validation. A similar FPR rate and true positive retention rate was achieved. Additional experiments showed that the
GA feature selection process integrated with the proposed data selection algorithm outperforms the one without it by
5%-10% FPR rate.
The methods proposed can be also applied to other application areas, such as computer-aided diagnosis of lung nodules.
The ability to summarize and abstract information will be an essential part of intelligent behavior in consumer devices. Various summarization methods have been the topic of intensive research in the content-based video analysis community. Summarization in traditional information retrieval is a well understood problem. While there has been a lot of research in the multimedia community there is no agreed upon terminology and classification of the problems in this domain. Although the problem has been researched from different aspects there is usually no distinction between the various dimensions of summarization. The goal of the paper is to provide the basic definitions of widely used terms such as skimming, summarization, and highlighting. The different levels of summarization: local, global, and meta-level are made explicit. We distinguish among the dimensions of task, content, and method and provide an extensive classification model for the same. We map the existing summary extraction approaches in the literature into this model and we classify the aspects of proposed systems in the literature. In addition, we outline the evaluation methods and provide a brief survey. Finally we propose future research directions based on the white spots that we identified by analysis of existing systems in the literature.
Current advanced television concepts envision data broadcasting along with the video stream, which is used by interactive applications at the client end. In this case, these applications do not proactively personalize the experience and may not allow user requests for additional information. We propose content enhancement using automatic retrieval of additional information based on video content and user interests. Our paper describes Video Retriever Genie, a system that enhances content with additional information based on metadata that provides semantics for the content. The system is based on a digital TV (Philips TriMedia) platform. We enhance content through user queries that define information extraction tasks that retrieve information from the Web. We present several examples of content enhancement such as additional movie character/actor information, financial information and weather alerts. Our system builds a bridge between the traditional TV viewing and the domain of personal computing and Internet. The boundaries between these domains are dissolving and this system demonstrates one effective approach for content enhancement. In addition, we illustrate our discussion with examples from two existing standards - MPEG-7 and TV-Anytime.
Today the consumers are facing an ever-increasing amount of television programs. The problem, however, is that the content of video programs is opaque. The existing video watching options for consumers are either to watch the whole video, fast forward to try and find the relevant portion, or to use electronic program guides to get additional information. In this paper we will present a summarization system for processing incoming video, extracting and analyzing closed caption text, determining the boundaries of program segments as well as commercial breaks and extracting a program summary from a complete broadcast to enable video transparency. The system consists of: transcript extractor, program type classifier, cue extractor, knowledge database, temporal database, inference engine, and summerizer. The main topics that will be discussed are video summary, video categorization and retrieval tools.
Consumer digital video devices are becoming computing platforms. As computing platforms, digital video devices are capable of crunching the compressed bits into the best displayable picture and delivering enhanced services. Although these deices will primarily aim to continue their traditional functions of display and storage, there are additional functions such as content management for real- time and stored video, tele-shopping, banking, Internet connectivity, and interactive services, which the device could also handle.
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