Computer-aided diagnosis (CAD) systems are indispensable tools for patients' healthcare in modern medicine.
Nevertheless, the only fully automatic CAD system available for lumbar stenosis today is for X-ray images. Its
performance is limited due to the limitations intrinsic to X-ray images. In this paper, we present a system for
magnetic resonance images. It employs a machine learning classification technique to automatically recognize
lumbar spine components. Features can then be extracted from these spinal components. Finally, diagnosis is done
by applying a Multilayer Perceptron. This classification framework can learn the features of different spinal
conditions from the training images. The trained Perceptron can then be applied to diagnose new cases for various
spinal conditions. Our experimental studies based on 62 subjects indicate that the proposed system is reliable and
significantly better than our older system for X-ray images.
Video-on-Demand is undoubtedly a promising technology for many important applications. Several periodic broadcast techniques have been proposed for the cost-effective implementation of such systems. However, the once-and-for-all implementation strategies of these broadcast schemes imply a common bandwidth requirement for all the clients. Multiresolution techniques address this issue by sacrificing video quality. We present an alternative approach which does not have this drawback. Our protocol, the HEterogeneous Receiver-Oriented (HeRO) Broadcasting, allows receivers of various communication capabilities to share the same periodic broadcast, and therefore enjoy the same video quality while requiring very little buffer space. This is achieved using a new data segmentation scheme with a surprising property. We present the broadcast technique, and compare its performance with that of existing methods.
Most content-based image retrieval techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image area or pre- perceived objects at the database build time. Recent approaches address this outstanding issue by allowing users to arbitrarily exclude noise in formulating their queries. This capability has resulted in high retrieval effectiveness for a wide range of queries. However, implementing these techniques for large image collections presents a great challenge since we can not assume any shape for queries defined by users. In this paper, we propose an efficient indexing/retrieval technique for arbitrarily-shaped queries which is able to eliminate a majority of unqualified images. Moreover, we improve the retrieval process with a filtering phase to prune out additional false matches before the detailed similarity measure is carried out. We have implemented the proposed technique in our image retrieval system for a large image collection. Our experimental results show that our technique is capable of handling image matching very well and 70 times on average faster than the straightforward sequential scanning.
Reducing noise in image query processing is no doubt one of the key elements to achieve high retrieval effectiveness. However, existing techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image are or pre-perceived objects at the database build time. In this paper we address this outstanding issue by proposing a similarity mode for noise- free queries. In our approach, users formulate their queries by specifying objects of interest, and image similarity is based only on these relevant objects. We discuss how our approach can handle translation and scaling matching as well as how space overhead can be minimized. Our experiments show that this approach, with 1/16 the storage overhead, outperforms techniques for rectangular queries and a related technique by a significant margin.
Shot boundary detection (SBD) is the first fundamental step to managing video databases. It segments video data into the basic units for indexing and retrieval. Many automatic SBD techniques exist. They, however, are based on sequential search, and therefore too expensive for practical use. To address this problem, we explore a different direction to SBD in this paper. We investigate a non-linear approach in which most video frames do not need to be compared. This idea is fundamentally different from all existing methods. In fact, it is orthogonal to these schemes in the sense that it can be applied to substantially improve their performance. Our experiments show that this idea speeds up a conventional method based on color histograms up to 16 times while preserving the same accuracy. On the average, the improvement is five time according to our experiments on 26 videos of six different types.
Despite advances in networking technology, the limitation of the server bandwidth prevents multimedia applications from taking full advantage of next-generation networks. This constraint sets a hard limit on the number of users the server is able to support simultaneously. To address this bottleneck, we propose a Caching Multicast Protocol (CMP) to leverage the in-network bandwidth. Our solution caches video streams in the routers to facilitate regional services in the immediate future. In other words, the network storage is managed as a huge `video server' to allow the application to scale far beyond the physical limitation of its video server. The tremendous increase in the service bandwidth also enables the system to provide true on-demand services. To assess the effectiveness of this technique, we develop a detailed simulator to compare its performance with that of our earlier scheme called Chaining. The simulation results indicates that CMP is substantially better with many desirable properties as follows: (1) it is optimized to reduce traffic congestion; (2) it uses much less caching space; (3) client workstations are not involved in the caching protocol; (4) it can work on the network layer to leverage modern routers.
Scene is considered a good unit for indexing and retrieving data from large video databases. In this paper, we present a new content-based approach for detecting and classifying scene changes in video sequences. Our technique can detect and classify not only abrupt changes (i.e., hard cuts) but also gradual changes such as fades and dissolves. We compute background difference between frames, and use background tracking to handle various camera motions. Although our method processes significantly less data, it results in more semantically rich pieces (i.e., scenes). Our experiments on various types of videos indicate that the proposed technique is much less sensitive to the predefined threshold values, and is very effective in reducing the number of false hits. Our approach is particularly suitable for very large video databases because it is both space and time efficient.
Patching has been shown to be cost efficient for video-on- demand systems. Unlike conventional multicast, patching is a dynamic multicast scheme which enables a new request to join an ongoing multicast. Since a multicast can now grow dynamically to serve new users, this approach is more efficiency than traditional multicast. In addition, since a new request can be serviced immediately without having to wait for the next multicast, true video-on-demand can be achieved. In this paper, we introduce the notion of patching window, and present a generalized patching method. We show that existing schemes are special cases with a specific patching window size. We derive a mathematical formula to help determine the optimal size for the patching window. This formula allows us to design the best patching scheme given a workload. The proposed technique is validated using simulations. They show that the analytical results are very accurate. We also provide performance results to demonstrate that the optimal technique outperforms the existing schemes by a significant margin. It is also up to two times better than the best Piggybacking method which provides data sharing by merging the services in progress into a single stream by altering their display rates.