Automatic segmentation of lung areas based on SNAKES and extraction of abnormal areas

Itai, Y; Kim, Hyoungseop; Ishikawa, Seiji; Katsuragawa, Shigehiko; Ishida, Takayuki; Nakamura, Kenji; Yamamoto, Akira

doi:10.1109/ictai.2005.44

Cited by 46 publications

(31 citation statements)

References 9 publications

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“…In [77], the segmentation algorithm is applied based Sobel edge detection method, in order to detect the cancer nodules from the extracted lung image, whereas, in [44], the snake algorithm was used to extract the nodules' boundaries. Later, Tariq et al used gradient mean and variance based method for the extraction of lung background since gradient operator has high values for pixels belonging to the boundary between foreground and background [82].…”

Section: ) 2d-based Approachesmentioning

confidence: 99%

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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Abstract-Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient's chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

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Section: ) 2d-based Approachesmentioning

confidence: 99%

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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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 deformable model starts from an initial segmentation which was obtained by a threshold estimated from CT data. The segmentation results were used as an initial step to classify abnormal areas within each lung field [19]. A 2D geometric active contour was initialized along the boundary of the chest region, which was then automatically split into two regions representing the left and right lungs in [20].…”

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

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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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“…Respiratory system segmentation [13] was used for the process of removing the trachea and primary bronchi present in the CT lung image. Itai et al [14] developed a method using snake segmentation method. Snake is one of the most popular active contour models which defines an initial curve within the image and then it grow up to reach the object boundary under the influences of the internal and external forces computed based on image statistics information.…”

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confidence: 99%