Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Pulagam, Ammi Reddy; Kande, Giri Babu; Ede, Venkata Krishna Rao; Inampudi, Ramesh Babu

doi:10.1007/s10278-016-9875-z

Cited by 38 publications

(25 citation statements)

References 31 publications

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“…Noor et al [11] Framework including thresholding and morphology based segmentation coupled with feedback -----98.4% Pulagam et al [12] The modified convex hull approach and morphological operators -----98.62%…”

Section: Visual Results and Interpretationsmentioning

confidence: 99%

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Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Koyuncu

2018

ijisae

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Lung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant part of a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose, parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lung parenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imaging and nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shift segmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20 axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluate the performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison is realized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity), 99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.

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Section: Visual Results and Interpretationsmentioning

confidence: 99%

“…In experiments, it was revealed that proposed method performed the segmentation with high DSC (98.4%). Pulagam et al [12] utilized the modified convex hull approach and morphological operators for segmentation. According to the experiments, proposed approach obtained 98.62% DSC on overall.…”

Section: Introductionmentioning

confidence: 99%

Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Koyuncu

2018

ijisae

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“…For over a decade, scholars from various strata of the globe have put forward a series of pulmonary lung nodule detection methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The primary stages employed in these lung nodule detection methods are lung segmentation [4][5][6][7][8][9][10][11][12][13], nodule candidate detection [14][15][16][17][18][19], and elimination of false positive nodules (FPNs) [10,14,[19][20][21]. The first stage shows that lungs look like dark regions in CT scans, as they are basically bags full of air inside it, hence, the image intensities of the lung and surrounding tissues are clearly contrasted.…”

Section: Previous Workmentioning

confidence: 99%

“…To overcome these problems, a chain code representation method [10], morphological approaches [11], a rolling ball method [12] are used. In this paper a new contour correction method is proposed by using modified convex hull algorithm [13] for effective segmentation of lung region.…”

Section: Previous Workmentioning

confidence: 99%

Automated Pulmonary Lung Nodule Detection Using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier

Pulagam¹,

Ede²,

Inampudi

2017

JBEMI

Self Cite

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The pulmonary lung nodule is the most common indicator of lung cancer. An efficient automated pulmonary nodule detection system aids the radiologists to detect the lung abnormalities at an early stage. In this paper, an automated lung nodule detection system using a feature descriptor based on optimal manifold statistical thresholding to segment lung nodules in Computed Tomography (CT) scans is presented. The system comprises three processing stages. In the first stage, the lung region is extracted from thoracic CT scans using gray level thresholding and 3D connected component labeling. After that novel lung contour correction method is proposed using modified convex hull algorithm to correct the border of a diseased lung. In the second stage, optimal manifold statistical image thresholding is described to minimize the discrepancy between nodules and other tissues of the segmented lung region. Finally, a set of 2D and 3D features are extracted from the nodule candidates, and then the system is trained by employing support vector machines (SVM) to classify the nodules and non-nodules. The performance of the proposed system is assessed using Lung TIME database. The system is tested on 148 cases containing 36408 slices with total sensitivity of 94.3%, is achieved with only 2.6 false positives per scan.

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“…It is estimated that by 2025, the number of people who die of lung cancer in China alone will be close to 1 million each year. Studies have shown that early detection and early treatment of lung cancer can effectively improve the survival rate of lung cancer patients: the 5-year survival rate increased from 14% to 49% [1]. CT imaging is one of the effective methods to help doctors diagnose lung diseases.…”

Section: Introductionmentioning

confidence: 99%

Lung segmentation method with dilated convolution based on VGG-16 network

Geng

Zhang

Tong

et al. 2019

Computer Assisted Surgery

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Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accurately segmenting the lung parenchyma, this paper proposes a segmentation method based on the combination of VGG-16 and dilated convolution. First of all, we use the first three parts of VGG-16 network structure to convolution and pooling the input image. Secondly, using multiple sets of dilated convolutions make the network has a large enough receptive field. Finally, the multi-scale convolution features are fused, and each pixel is predicted using MLP to segment the parenchymal region. Experimental results were produced over state of the art on 137 images which key metrics Dice similarity coefficient (DSC) is 0.9867. Experimental results show that this method can effectively segment the lung parenchymal area, and compared to other conventional methods better.

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Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

Cited by 38 publications

References 31 publications

Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Automated Pulmonary Lung Nodule Detection Using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier

Lung segmentation method with dilated convolution based on VGG-16 network

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