Abstract
We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.
Original language | English |
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Title of host publication | Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings |
Pages | 413-420 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |
Event | 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008 - Tokyo, Japan Duration: 2008 Aug 1 → 2008 Aug 2 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 5128 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008 |
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Country/Territory | Japan |
City | Tokyo |
Period | 08/8/1 → 08/8/2 |
Bibliographical note
Funding Information:This work is supported by Science and Technology Commission of Shanghai Municipality of China (Grant number: 06dz22103).
Keywords
- Active shape model
- Hierarchical principal component analysis
- Scale space analysis
- Statistical model
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science