An Efficient Method of Automatic Pulmonary Parenchyma Segmentation in CT Images

Chen, Zhaoxue; Sun, Xiaochun; Nie, Shengdong

doi:10.1109/iembs.2007.4353601

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Chen et al [12] used image threshold, fast flood filling technique, erosion operation, and an area-filtering step to segment lung regions. Geng et al [13] used 3D region growing with an iterative method to segment lung regions.…”

Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Sasidhar

Rao

Babu

et al. 2016

Emerging Research in Computing, Information, Communication and Applications

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Segmentation of lung regions with lung nodules at mediastinum is the first step in computer-aided detection (CAD) which provides a better diagnosis of lung cancer. The existing methods fail in segmentation of lung regions with the cancer tumors at the mediastinum of the lungs. In this paper, a new approach is proposed that extracts lung regions with cancer tumors at the mediastinum of the lungs based on curve analysis. The proposed algorithm is tested on 05 patient's dataset which consists of 60 images of the Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 99 % average overlap measure (AΩ). The proposed algorithm extracts lung nodules at the mediastinum of the lungs which helps in detection of lung cancer.

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Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Sasidhar

Rao

Babu

et al. 2016

Emerging Research in Computing, Information, Communication and Applications

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“…Chen et al [11] in their work have proposed a method for automatic pulmonary parenchyma segmentation in CT images. They have used a mid-value nonlinear filter for noise removal.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

et al. 2012

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Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computeraided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.

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“…Armato and Sensakovic [11] tracks the boundary to segment lung profiles. Otsu is utilized to automatically segment pulmonary parenchyma in [12]. The work segments parts of the lung, but the erosion operation which is used to fill to holes in the lung image always loss image information.…”

Section: Introductionmentioning

confidence: 99%

A novel lung cancer detection algorithm for CADs based on SSP and Level Set

Zhu

Chun-Hyok

Song

et al. 2017

THC

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Abstract. The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image. The super-pixels method and a level set method are used to segment the suspected lung cancer lesion region simultaneously. Finally, the cancer is determined by the difference between results of the two segmentation methods. Experimental results show that the proposed algorithm has a high accuracy for lung cancer detection in CT images. For gross glass nodule, pleural nodule, the vascular nodules and solitary nodules, the sensitivity of the detection algorithm are respectively 91.3%, 96.3%, 80.9% and 82.3%.

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An Efficient Method of Automatic Pulmonary Parenchyma Segmentation in CT Images

Cited by 8 publications

References 2 publications

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis

A novel lung cancer detection algorithm for CADs based on SSP and Level Set

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