Automatic fetal organs detection and approximation in ultrasound image using boosting classifier and hough transform
2014 (English)In: Proceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems, 2014, 460-467 p.Conference paper (Refereed)
In this paper we proposed a system for automatic fetal detection and approximation in ultrasound image. We used Adaboost. MH based on Multi Stump Classifier to detect fetal organs in ultrasound. After fetal organ detected, it is approximated using Randomized Hough Transform. Experiments result show that mean accuracy of the fetal organs detection reaches 93.92% with mean kappa coefficient value reaches 0.854 and mean hamming error reaches 0.032. Proposed method has better performance compared to other five methods proposed in previous researches. Fetal Organ shape approximation performance reaches 81% for fetal head, 57% for fetal abdomen, 72% of fetal femur, and 66% of fetal humérus.
Place, publisher, year, edition, pages
2014. 460-467 p.
Adaptive boosting, Feature extraction, Hough transforms, Image classification, Better performance, Boosting classifiers, Fetal head, Fetal organs, Kappa coefficient, Randomized Hough transform, Shape approximation, Ultrasound images, Ultrasonic applications
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-168858DOI: 10.1109/ICACSIS.2014.7065897ScopusID: 2-s2.0-84946689594ISBN: 9781479980758OAI: oai:DiVA.org:kth-168858DiVA: diva2:819961
2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014, 18 October 2014 through 19 October 2014
QC 201506112015-06-112015-06-092015-06-11Bibliographically approved