- Title
- Automatic lung field segmentation in x-ray radiographs using statistical shape and appearance models
- Creator
- Li, Xuechen; Luo, Suhuai; Hu, Qingmao; Li, Jiaming; Wang, Dadong; Chiong, Fabian
- Relation
- Journal of Medical Imaging and Health Informatics Vol. 6, Issue 2, p. 338-348
- Publisher Link
- http://dx.doi.org/10.1166/jmihi.2016.1714
- Publisher
- American Scientific Publishers
- Resource Type
- journal article
- Date
- 2016
- Description
- General radiographs are an initial diagnostic tool for a variety of clinical conditions such as lung disease detection. The size, shape and texture of a lung field are key parameters for X-ray radiographs based lung disease diagnosis in which the lung field segmentation is a significant step. Although many new methods have been proposed in medical image applications, the lung field segmentation remains a challenge. In this paper, we have proposed an improved segmentation method based on statistical shape and appearance models. For the shape model, multi-scale and multi-step-size with different limitation parameters were used to increase the searching ability. For the appearance model, multiple features with different weights were used to describe different parts of the lung field border. A set of 247 chest radiographs was used to test the method. The average overlap of the proposed method was 93.1% for the publicly available JSRT database. The experiment results show that the proposed method outperforms other active shape model based methods.
- Subject
- chest radiograph; lung field segmentation; active shape model; appearance model; principal component analysis
- Identifier
- http://hdl.handle.net/1959.13/1323546
- Identifier
- uon:24834
- Identifier
- ISSN:2156-7018
- Language
- eng
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