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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Slabaugh, Greg; Yang, Xiaoyun; Ye, Xujiong; Boyes, Richard; Beddoe, Gareth (2010)
Publisher: Molecular Diversity Preservation International
Journal: Algorithms
Languages: English
Types: Article
Subjects: CAD, Electronic computers. Computer science, T55.4-60.8, QA75, G400 Computer Science, colorectal lesion detection, QA75.5-76.95, pattern recognition, Industrial engineering. Management engineering
Identifiers:doi:10.3390/a3010021
We present a complete, end-to-end computer-aided detection (CAD) system for identifying lesions in the colon, imaged with computed tomography (CT). This system includes facilities for colon segmentation, candidate generation, feature analysis, and classification. The algorithms have been designed to offer robust performance to variation in image data and patient preparation. By utilizing efficient 2D and 3D processing, software optimizations, multi-threading, feature selection, and an optimized cascade classifier, the CAD system quickly determines a set of detection marks. The colon CAD system has been validated on the largest set of data to date, and demonstrates excellent performance, in terms of its high sensitivity, low false positive rate, and computational efficiency.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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