Lung Segmentation on Postero-anterior Digital Chest Radiographs Using Active Contours

Iglesias, Isaac; Tahoces, Pablo G.; Souto, Miguel Abel; Alegría, Anxo Martínez de; Lado, Marı́a J.; Vidal, José Martı́nez

doi:10.1007/978-3-540-27868-9_58

Cited by 4 publications

(7 citation statements)

References 23 publications

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“…the average grey-level of its rows, per column, is estimated. Radiographic image profiles have been proved valuable in several radiographic image analysis tasks [1], [4], [6], [7], [10] as they provide a simple though effective means to navigating through a radiograph by using only one-dimensional information and elementary signal processing techniques. The proposed methodology utilizes horizontal profiles of subsequent image samples in order to obtain spatial instances of the radiograph from which the spinal cord and the ribcage boundaries are detectable even if the patient is bent.…”

Section: ) Spinal Cord Detection and Feature Points Of The Outer Lefmentioning

confidence: 99%

“…The boundaries of the lung fields are approximated by consecutive intuitively manipulated parametric curves. In contrast to the supervised state of the art methodologies [5], [15], it features unsupervised operation, it is not limited by patients' positioning as most other rule-based approaches do [6]- [ 10] and unlike edge-based methods [18] it is tolerant to boundary discontinuities which may arise from severe consolidations or low image quality.…”

Section: Introductionmentioning

confidence: 99%

“…Since then, a variety of approaches have been proposed for automatic detection of the lung fields and approximation of their boundaries. The most popular methods rely on rules on the locations and on the intensity profiles of the anatomic structures in conjunction with image processing techniques such as contrast enhancement, edge detection, merging and splitting operations [6]- [ 10]. Other methods include machine learning algorithms [11]- [13], active shape models [14]- [17], and graph cuts [18].…”

Section: Introductionmentioning

confidence: 99%

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Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Iakovidis

2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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The most common radiographic manifestation of bacterial pulmonary infections are foci of consolidation which are visible as bright shadows interfering with the interior lung intensity. In critically-ill patients this interference can be severe leading to vague or invisible lung field boundaries which are difficultly distinguished even by experienced physicians. This problem is amplified if the radiographs are of low quality as obtained with a portable x-ray device, routinely used in intensive care units. This paper proposes a pioneering methodology that copes with lung field detection in both stationary and portable chest radiographs by combining statistical grey-level intensity information and directional edge maps. The boundaries of the lung fields are approximated by consecutive intuitively manipulated parametric curves. Conventional and state of the art lung field detection approaches address only stationary radiography, and only a few of them cope with pulmonary infections. The proposed methodology features unsupervised operation, it is not iterative, it is not limited by the patients' positioning, and it is tolerant to the presence of consolidations and boundary discontinuities of the lung fields. Its performance is validated on various stationary radiographs and on a set of portable radiographs obtained from patients with bacterial pulmonary infections.

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Section: ) Spinal Cord Detection and Feature Points Of The Outer Lefmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Iakovidis

2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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“…Since the beginning of the 1960s, a variety of computational approaches to the detection of the lung field boundaries in plain chest radiographs have been proposed. These include rule-based methodologies [6][7][8][9][10][11][12], neural network, Markov random fields and other pattern classification approaches [1,13,14], active contour models [15], active shape and active appearance models [16] and graph cuts [17].…”

Section: Introductionmentioning

confidence: 99%

“…The studies [6][7][8][9][10][11] indicate that the detection performance of most rule-based methodologies can degrade if the quality of the radiograph is poor or if the positioning of the patient deviates from the standard. Recently, a rule-based approach coping with the issue of positioning has been proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding

Iakovidis

Savelonas

Papamichalis

2009

Meas. Sci. Technol.

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Portable chest radiography is a valuable tool for screening patients hospitalized in intensive care, providing visual cues for diagnosis and physiological measurements. However, its practicality comes at the cost of quality, which is mainly affected by misaligned body positioning, thus increasing x-ray misinterpretation rates. This paper presents a novel methodology for the detection of the lung field boundaries in portable chest radiographs of patients with bacterial pulmonary infections. Such infections are radiographically manifested as foci of consolidations which can lead to vague or invisible lung field boundaries, difficult to distinguish even by experienced physicians. Conventional and state-of-the-art approaches address mainly stationary radiographs, whereas only a few of them cope with pulmonary infections. The proposed methodology is based on an active shape model incorporating shape prior information about the lung fields. The model is initialized by a novel technique utilizing a set of salient points detected on the peripheral anatomic structures of the lungs. A selective thresholding algorithm based on a spinal cord sampling process supports both the initialization and the evolution of the model for the detection of the lung field boundaries. The experiments show that the proposed methodology outperforms state-of-the-art approaches.

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An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs

Zhu

et al. 2010

J Digit Imaging

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This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest-based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The results show that the classification performances are high. Compared with the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic.

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Lung Segmentation on Postero-anterior Digital Chest Radiographs Using Active Contours

Cited by 4 publications

References 23 publications

Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding

An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs

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