An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer

In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

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Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.20210401.oa5 ◽

2021 ◽

Vol 16 (2) ◽

pp. 87-104

Author(s):

Vijayalaxmi Mekali ◽

Girijamma H. A.

Keyword(s):

Lung Parenchyma ◽

Ct Images ◽

Experimental Result ◽

Computational Time ◽

Detection Rates ◽

Iterative Thresholding ◽

Automated Method ◽

Segmentation Approach ◽

Thresholding Algorithm ◽

Lung Lobes

Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.

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Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting

Journal of Healthcare Engineering ◽

10.1155/2017/5817970 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Vladimir Zlokolica ◽

Lidija Krstanović ◽

Lazar Velicki ◽

Branislav Popović ◽

Marko Janev ◽

...

Keyword(s):

Automatic Segmentation ◽

User Interaction ◽

Ct Images ◽

Epicardial Fat ◽

Risk Level ◽

Good Correspondence ◽

Fuzzy C Means ◽

High Dependency ◽

Segmentation Approach ◽

Fuzzy C Means Clustering

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians.

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