MARC 主機 00000nam  2200433   4500 
001    AAI1497142 
005    20120530083726.5 
008    120530s2011    ||||||||||||||||| ||eng d 
020    9781124794495 
035    (UMI)AAI1497142 
040    UMI|cUMI 
100 1  Wu, Hong 
245 10 Offline and Online Adaboost for Detecting Anatomic 
300    66 p 
500    Source: Masters Abstracts International, Volume: 50-01, 
       page: 0497 
500    Adviser: Jianming Liang 
502    Thesis (M.S.)--Arizona State University, 2011 
520    Detecting anatomical structures, such as the carina, the 
       pulmonary trunk and the aortic arch, is an important step 
       in designing a CAD system of detection Pulmonary Embolism 
520    The presented CAD system gets rid of the high-level prior 
       defined knowledge to become a system which can easily 
       extend to detect other anatomic structures. The system is 
       based on a machine learning algorithm --- AdaBoost and a 
       general feature --- Haar. This study emphasizes on off-
       line and on-line AdaBoost learning. And in on-line 
       AdaBoost, the thesis further deals with extremely 
       imbalanced condition 
520    The thesis first reviews several knowledge-based detection
       methods, which are relied on human being's understanding 
       of the relationship between anatomic structures. Then the 
       thesis introduces a classic off-line AdaBoost learning. 
       The thesis applies different cascading scheme, namely 
       multi-exit cascading scheme. The comparison between the 
       two methods will be provided and discussed 
520    Both of the off-line AdaBoost methods have problems in 
       memory usage and time consuming. Off-line AdaBoost methods
       need to store all the training samples and the dataset 
       need to be set before training. The dataset cannot be 
       enlarged dynamically. Different training dataset requires 
       retraining the whole process. The retraining is very time 
       consuming and even not realistic 
520    To deal with the shortcomings of off-line learning, the 
       study exploited on-line AdaBoost learning approach. The 
       thesis proposed a novel pool based on-line method with 
       Kalman filters and histogram to better represent the 
       distribution of the samples' weight. Analysis of the 
       performance, the stability and the computational 
       complexity will be provided in the thesis 
520    Furthermore, the original on-line AdaBoost performs badly 
       in imbalanced conditions, which occur frequently in 
       medical image processing.  In image dataset, positive 
       samples are limited and negative samples are countless. A 
       novel Self-Adaptive Asymmetric On-line Boosting method is 
       presented. The method utilized a new asymmetric loss 
       criterion with self-adaptability according to the ratio of
       exposed positive and negative samples and it has an 
       advanced rule to update sample's importance weight taking 
       account of both classification result and sample's label. 
       Compared to traditional on-line AdaBoost Learning method, 
       the new method can achieve far more accuracy in imbalanced
590    School code: 0010 
650  4 Engineering, Biomedical 
650  4 Biology, Bioinformatics 
650  4 Computer Science 
690    0541 
690    0715 
690    0984 
710 2  Arizona State University.|bComputing Studies 
773 0  |tMasters Abstracts International|g50-01 
856 40 |u