@inproceedings{83a036ddb8f94662a5b7c33a43bf62c7,
title = "Automatic fetal organs detection and approximation in ultrasound image using boosting classifier and hough transform",
abstract = "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 hum{\'e}rus.",
author = "Ma'Sum, {M. Anwar} and Wisnu Jatmiko and Tawakal, {M. Iqbal} and Afif, {Faris Al}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014 ; Conference date: 18-10-2014 Through 19-10-2014",
year = "2014",
month = mar,
day = "23",
doi = "10.1109/ICACSIS.2014.7065897",
language = "English",
series = "Proceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "460--467",
booktitle = "Proceedings - ICACSIS 2014",
address = "United States",
}