Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

Xiao, Runing; Zhou, Jinzhi

doi:10.3390/a12040075

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The fuzzy C-means method attributes clusters to regions based on a pre-defined regional feature calculation [49][50][51][52][53][54][55]. Some methods are based on the wavelet transformation, commonly used to highlight desired frequencies in the image [46,[56][57][58][59][60][61][62]. Several methods are mainly based on the region growing principle but include techniques from other types of methods such as Markov random fields, gaussian mixture model, graph cut, unsupervised k-means, Kapur's entropy, convex hull, random walker and others .…”

Section: Region-based Methodsmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

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Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

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Section: Region-based Methodsmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

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Pulmonary fissure segmentation in CT images based on ODoS filter and shape features

Peng

Luan

et al. 2023

Multimed Tools Appl

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Improvised grasshopper algorithm for automatic lung disease detection

Sehgal

Agarwal

Gupta

et al. 2022

IDT

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Chronic obstructive pulmonary disease (COPD) has been impacting a large population. It has a higher fatality rate than that of lung cancer. Diagnosis of this disease is quite challenging. Medical images analysis has been able to solve this challenge by early and accurate diagnosis of pulmonary disease. This analysis technique helps in pre-diagnosis and providing timely medical treatment thus reducing the mortality rate. The goal of this study is to establish an accurate process for classifying CT scan images into healthy lungs, COPD and Fibrosis impacted lung images. This classifying process has three steps. In the first step, lung scan is used for feature extraction. Then second and third step of feature selection and lung disease identification are carried using Machine Learning (ML) classifier. Haralick texture features with Gray Level Co-occurrence Matrix (GLCM), Zernike’s moments, Gabor features and spatial domain features are used for feature extraction from the segmented lung CT images. For feature selection, our proposed evolutionary algorithm is the Improvised Grasshopper Algorithm (IGOA). After feature extraction from CT scan medical images, IGOA selects an optimal set of features that increases the classification accuracy and decreases the cost of computation. Lastly, three ML classifiers viz. Decision Tree Classifier, k-Nearest Neighbor (KNN), Random Forest Classifier are applied to every feature set chosen by IGOA. The research results show that IGOA filtered out the maximum number of unimportant features of about 71.01%. IGOA eliminates 28.99% of the total extracted features. IGOA gave a better accuracy of 99.8%. Research results imply that the introduced feature selection method is appropriate for disease classification from CT scan images. IGOA method can be used for real-time applications as it has a less computational cost and has better accuracy.

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Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

Cited by 3 publications

References 21 publications

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

Pulmonary fissure segmentation in CT images based on ODoS filter and shape features

Improvised grasshopper algorithm for automatic lung disease detection

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