Automatic segmentation of the lung fields in portable chest radiographs based on Bézier interpolation of salient control points

Iakovidis, Dimitris K.; Papamichalis, George

doi:10.1109/ist.2008.4659946

Cited by 16 publications

(31 citation statements)

References 13 publications

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“…The radiographic images were 8-bit grayscale with a size of 2816×2112 pixels. The lung fields were isolated using the methodology proposed in [5] and were divided into 32×32 sub-images. From each sub-image an intensity histogram signature was calculated so as to build the data matrix that was used as input to the NMF.…”

Section: Resultsmentioning

confidence: 99%

“…The diversity and the complexity of the visual content of the lung fields as well as the quality variability induced by the variable parameters of the radiation exposure, make its medical interpretation a challenging task. This task has motivated many researchers to develop computational methods for automatic lung field detection and analysis [4] [5].…”

Section: Introductionmentioning

confidence: 99%

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Mining Patterns of Lung Infections in Chest Radiographs

Tsevas

Iakovidis

Papamichalis

2009

IFIP Advances in Information and Communication Technology

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Chest radiography is a reference standard and the initial diagnostic test performed in patients who present with signs and symptoms suggesting a pulmonary infection. The most common radiographic manifestation of bacterial pulmonary infections is foci of consolidation. These are visible as bright shadows interfering with the interior lung intensities. The discovery and the assessment of bacterial infections in chest radiographs is a challenging computational task. It has been limitedly addressed as it is subject to image quality variability, content diversity, and deformability of the depicted anatomic structures. In this paper, we propose a novel approach to the discovery of consolidation patterns in chest radiographs. The proposed approach is based on non-negative matrix factorization (NMF) of statistical intensity signatures characterizing the densities of the depicted anatomic structures. Its experimental evaluation demonstrates its capability to recover semantically meaningful information from chest radiographs of patients with bacterial pulmonary infections. Moreover, the results reveal its comparative advantage over the baseline fuzzy C-means clustering approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Patterns of Lung Infections in Chest Radiographs

Tsevas

Iakovidis

Papamichalis

2009

IFIP Advances in Information and Communication Technology

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“…The radiographs are first normalized using the spinal cord as a reference point. Afterwards, the lung fields are isolated in regions of interest (ROIs) defined either manually or with a pre-processing lung field boundary detection algorithm [9]- [10]. The proposed image analysis methodology is applied only in these ROIs.…”

Section: Methodsmentioning

confidence: 99%

“…In order to detect the region of the spinal cord each radiograph is uniformly sampled from top to bottom using nonoverlapping rectangular sub-images that were further processed according to [9]- [10]. For each sample an average horizontal profile, i.e.…”

Section: A Normalizationmentioning

confidence: 99%

“…More specifically we consider the region of the spinal cord/mediastinum as a reference region for normalization. The reasons motivated our approach include: (a) this region can be easily detected since it lies roughly somewhere in the middle of the radiograph and it is generally characterized by higher intensities than other regions in a chest radiograph [9]- [10], (b) though the deviation of the absolute intensity features may considerably vary between different radiographs, the relative intensities of the different anatomic structures within each image, considering the spinal cord/mediastinum region as a reference, are rather constant. This is explained by the nature of This work was supported in part by the European Commission's Seventh Framework Information Society Technologies (IST) Programme, Unit ICT for Health, project DEBUGIT (no.…”

Section: Introductionmentioning

confidence: 99%

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Patient specific normalization of chest radiographs and hierarchical classification of bacterial infection patterns

Tsevas

Iakovidis

2010

2010 IEEE International Conference on Imaging Systems and Techniques

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The analysis of multiple chest radiographs is prone to errors mainly due to different acquisition settings used in clinical routine and due to patient specific characteristics affecting the distribution of the image intensities. The purpose of this paper is twofold: (a) it proposes a new image normalization technique based on samples extracted from the spinal cord/mediastinum region, in order to reduce the error due to the different settings and the patients' characteristics; (b) it proposes a supervised hierarchical classifier combination scheme fusing knowledge extracted from intensity histograms and Gabor textural features so as to cope with the detection of lung consolidations, and the assessment of their extent. The presence of such consolidations in a chest radiograph is usually an indication of a bacterial pulmonary infection. The experimentation results validate the advantage of the proposed normalization technique over the conventional ones and the effectiveness of the hierarchical classification of the normalized patterns.

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A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

Ullah

Ali

Shah

et al. 2023

Sci Rep

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Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods.

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Automatic segmentation of the lung fields in portable chest radiographs based on Bézier interpolation of salient control points

Cited by 16 publications

References 13 publications

Mining Patterns of Lung Infections in Chest Radiographs

Mining Patterns of Lung Infections in Chest Radiographs

Patient specific normalization of chest radiographs and hierarchical classification of bacterial infection patterns

A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

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