A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Simultaneous multi-frame subpixel boundary definition using toboggan-based intelligent scissors for image and movie editing
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Pulmonary embolism (PE) is a very serious condition causing sudden death in about one-third of the cases. Treatment with anti-clotting medications is highly effective but not without complications, while diagnosis has been missed in about 70% of the cases. A major clinical challenge, particularly in an Emergency Room, is to quickly and correctly diagnose patients with PE and then send them on to therapy. Computed tomographic pulmonary angiography (CTPA) has recently emerged as an accurate diagnostic tool for PE, but each CTPA study contains hundreds of CT slices. The accuracy and efficiency of interpreting such a large image data set is complicated by various PE look-alikes and also limited by human factors, such as attention span and eye fatigue. In response to this challenge, in this paper, we present a fast yet effective approach for computer aided detection of pulmonary embolism in CTPA. Our proposed approach is capable of detecting both acute and chronic pulmonary emboli with a distinguished feature of incrementally reporting any detection immediately once becoming available during searching, offering real-time support and achieving 80% sensitivity at 4 false positives. This superior performance is contributed to our novel algorithms (concentration oriented tobogganing and multiple instance classification) introduced in this paper for candidate detection and false positive reduction.