An Level Set Evolution Morphology Based Segmentation of Lung Nodules and False Nodule Elimination by 3D Centroid Shift and Frequency Domain DC Constant Analysis

Krishnamurthy, Senthilkumar; Narasimhan, Ganesh; Umamaheswari, R.

doi:10.14257/ijunesst.2016.9.10.18

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of computer-aided diagnosis, the information of two-dimensional (2D) CT images is usually the main one, and there is no multidimensional information [ 11 , 12 ]. Therefore, multidimensional information makes the diagnosis rate of lung cancer higher.…”

Section: Introductionmentioning

confidence: 99%

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

et al. 2020

View full text Add to dashboard Cite

In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what's good and what's bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on Stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

et al. 2020

View full text Add to dashboard Cite

show abstract

An Level Set Evolution Morphology Based Segmentation of Lung Nodules and False Nodule Elimination by 3D Centroid Shift and Frequency Domain DC Constant Analysis

Abstract: A Level Set Evolution with Morphology (LSEM) based segmentation algorithm is proposed in this work

Cited by 1 publication

References 23 publications

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

Texture recognition of pulmonary nodules based on volume local direction ternary pattern

Contact Info

Product

Resources

About