A region based active contour method for x-ray lung segmentation using prior shape and low level features

Annangi, Pavan; Thiruvenkadam, Sheshadri; Raja, Aamir; Xu, Haiyan; Sun, Xiaochun; Mao, Ling

doi:10.1109/isbi.2010.5490130

Cited by 75 publications

(53 citation statements)

References 13 publications

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“…These methods can be generally divided into the following four major categories: threshold method [11][12][13][14][15][16][17][18], deformable boundary models [19][20][21][22][23][24], edge-based methods [25][26][27][28], and registration-based method [29,30]. Lungs appear as dark regions in CT scans, since they are essentially bags full of air inside the body.…”

Section: Previous Workmentioning

confidence: 99%

“…The shape-based techniques add prior information about the lung shape to image signals, for improving the segmentation accuracy. Integrating a prior shape term [21], with a term describing edge feature points and a term representing region-based data statistics in a variation energy framework for lung segmentation, is calculated as described in [22]. In order to segment the lung fields from posterior-anterior (PA) chest X-ray images, the formulated energy was used to guide a level set deformable model.…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Pulagam¹,

Kande²,

Ede³

et al. 2016

J Digit Imaging

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Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics d mean = 1.39 mm, and d rms = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Pulagam¹,

Kande²,

Ede³

et al. 2016

J Digit Imaging

View full text Add to dashboard Cite

show abstract

“…Another method [10] uses graph cut techniques and formulates the segmentation into an optimization problem; it is similar to the above method [9] in that it uses a classifier for identifying disease cases after feature extraction. Other approaches include rule-based processing [11,12], pixel classification [13], and the active contour method [14]. Xu and Doi [11,12] proposed an algorithm for the identification of the rib cage boundary and the edges of the hemidiaphragms that uses an intensity-profile-based method.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Lung Field Segmentation in Scoliosis Radiographs: An Exploratory Study

et al. 2015

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Scoliosis has a detrimental effect on lung function. Since spine radiographs are commonly used for monitoring the progress of the disorder, semi-automated lung field segmentation from scoliosis radiographs is a primary step in further automation and processing. Existing lung field segmentation algorithms have been developed specifically for chest radiographs, which differ from spine radiographs in imaging aspects and appearance. The present work uses intensity profile processing followed by the optimization of a flexible polynomial template developed without prior training data. Right and left lungs are processed separately due to the presence of the cardiac shadow and differences in lung shape. Intensity profile processing is based on identification of the maxima and minima in segments of the horizontal profiles or their derivatives. The polynomial template represents an implicit shape model defined separately for each lung using three initial points chosen by the user. Thus, a unique template is generated for each case. The algorithm was tested on six sets of patient radiographs varying in severity of scoliosis and image quality. The proposed method successfully detects overlapping regions of the lung and the spine and has high accuracy even for severely deformed lungs.

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“…Annangi et al in [21], proposed a regionbased active contour method for x-ray lung segmentation using prior shape and low level features. Wu, Wang and Jia in [22], presented a novel external force, called adaptive diffusion flow (ADF), with adaptive diffusion strategies according to the characteristics of an image region in the parametric active contour model framework.…”

Section: Introductionmentioning

confidence: 99%

3D segmentation and visualization of lung and its structures using CT images of the thorax

Filho¹,

Cortez²,

Albuquerque³

2013

JBiSE

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Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

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A region based active contour method for x-ray lung segmentation using prior shape and low level features

Cited by 75 publications

References 13 publications

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Semi-automated Lung Field Segmentation in Scoliosis Radiographs: An Exploratory Study

3D segmentation and visualization of lung and its structures using CT images of the thorax

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