A Fast Automatic Method of Lung Segmentation in CT Images Using Mathematical Morphology

Li, W.; Nie, Sheng Dong; Cheng, Jian

doi:10.1007/978-3-540-36841-0_610

Cited by 15 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods for lung segmentation have been proposed over the last two decades. Conventional methods rely on techniques such as thresholding [27,28], region growing [29,30], active contours [31,32], mathematical morphology [33,34], and cluster analysis [35][36][37]; however, deep learning (DL) approaches, in particular convolutional neural networks [38,39], generative adversarial networks [40,41], and residual neural networks [42], have recently gained popularity in this field as well. While DL methods achieve state-ofthe-art accuracy, a drawback of these methods is that they require substantial amounts of annotated training data to achieve the desired accuracy.…”

Section: Application: Lung Segmentation In Ct Scansmentioning

confidence: 99%

Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

Tóth,

Tomán,

Hajdu

et al. 2023

Mathematics

View full text Add to dashboard Cite

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F1 score of 0.9397 and an associated MCC of 0.7757. Our proposed method maintained the solution quality with an F1 score of 0.9395 and MCC of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold.

show abstract

Section: Application: Lung Segmentation In Ct Scansmentioning

confidence: 99%

Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

Tóth,

Tomán,

Hajdu

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The quantitative analysis is based on four statistical performance parameters which are accuracy, precision, recall, and F-score that are primarily used in image segmentation studies as described in research papers [11], [13], [18], [24], [25]. The accuracy test determines how well a diagnostic test identifies and rules out a specific condition.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The CT image is used to record images and for the radiologists to perform diagnoses. Using a CT scan, several types of tissues such as lung, bone, soft tissues, and blood vessels can be shown with great clarity, which cannot be seen, in conventional ISSN: 2088-8708  radiographs [13]. These scans yield a large amount of image data.…”

Section: Introductionmentioning

confidence: 99%

A new procedure for lung region segmentation from computed tomography images

Abdullah

Sulaiman²,

Osman³

et al. 2022

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.</span>

show abstract

“…various parts [15]- [16]. The smallest lung cancer nodule sizes are all between 5 mm and 25 mm [17]- [18].…”

Section: I Pre-processingmentioning

confidence: 99%

Robust and Automated Lung Cancer Nodule Detection Using Image Processing Techniques

Premchander¹,

Rao²,

Kanth³

2018

ijarcs

View full text Add to dashboard Cite

show abstract

A Fast Automatic Method of Lung Segmentation in CT Images Using Mathematical Morphology

Cited by 15 publications

References 3 publications

Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

A new procedure for lung region segmentation from computed tomography images

Robust and Automated Lung Cancer Nodule Detection Using Image Processing Techniques

Contact Info

Product

Resources

About