An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm

Wang, Monan; Li, Donghui

doi:10.3390/diagnostics12122971

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…For this purpose, the automatic choice of seed points for the RGA was adapted to incorporate the use of prior knowledge on lung tumours. This enhanced the segmentation accuracy of the tumour region identification, which underlines the importance of choosing appropriate positions for the seed points in RGA applications [32].…”

Section: Algorithms For Region Analysismentioning

confidence: 85%

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Meyer zu Westerhausen,

Raveendran,

Lauth

et al. 2024

Sensors

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Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results.

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Section: Algorithms For Region Analysismentioning

confidence: 85%

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Meyer zu Westerhausen,

Raveendran,

Lauth

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…For this purpose, the automatic choice of seed points for the RGA was adapted to incorporate the use of prior knowledge on lung tumor. This enhanced the segmentations accuracy of the tumor region identification, which underlines the importance to choose appropriate positions for the seed points in RGA applications [32].…”

Section: Algorithms For Region Analysismentioning

confidence: 87%

Reliability Assessment of Wireless Sensor Networks by strain-based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Meyer zu Westerhausen,

Raveendran,

Lauth

et al. 2024

Preprint

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Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

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“…In this model, the nonlinear ReLU activation function is applied and mathematically represented in Eq. (5).…”

Section: A Lobe Segmentation Phasementioning

confidence: 99%

“…Lung cancer segmentation methods are divided into two types: The first type comprises traditional techniques while the second type consists of deep learning (DL) techniques. Traditional techniques mostly centered on intensity-based methods such as region growing [4], [5], VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

et al. 2023

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Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

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An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm

Cited by 6 publications

References 33 publications

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Reliability Assessment of Wireless Sensor Networks by strain-based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

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