Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest

Shariaty, Faridoddin; Orooji, Mahdi; Velichko, Elena; Zavjalov, Sergey V.

doi:10.1016/j.compbiomed.2021.105086

Cited by 23 publications

(10 citation statements)

References 54 publications

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“…Savic et al [ 22 ] proposed a region-based fast marching method to achieve lung nodule segmentation. Shariaty et al [ 23 ] constructed a model to segment lung nodules from the perspective of texture features.…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Zhang¹,

Lin²,

Hui³

et al. 2022

Computational and Mathematical Methods in Medicine

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Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors’ work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors’ diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor’s diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.

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Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Zhang¹,

Lin²,

Hui³

et al. 2022

Computational and Mathematical Methods in Medicine

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show abstract

“…To help physicians diagnose more accurately, various researchers have suggested a computer‐aided detection (CAD) method. For many CAD systems, there are two stages: extraction frames candidate and false positive rate reduction 16‐18 …”

Section: Introductionmentioning

confidence: 99%

“…The method first scans the image coarsely, extracting numerous suspicious nodules (high sensitivity, high false‐positive rate), and then transmits them to the second stage to be analyzed 19,20 . Other approaches include mathematical morphology, shape curvature, and intensity threshold 17 . A traditional method to minimize false alarms is to incorporate location, size, shape, density, texture, gradient, and upper and lower people information 21,22 .…”

Section: Introductionmentioning

confidence: 99%

“…For many CAD systems, there are two stages: extraction frames candidate and false positive rate reduction. [16][17][18] The method first scans the image coarsely, extracting numerous suspicious nodules (high sensitivity, high false-positive rate), and then transmits them to the second stage to be analyzed. 19,20 Other approaches include mathematical morphology, shape curvature, and intensity threshold.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

Musthafa¹,

Sankar

Benil

et al. 2022

Concurrency and Computation

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Lung cancer is one of the major causes of death in the world, according to radiologists. However, a constant flow of medical images to hospitals is forcing radiologists to focus on accurate early prediction of nodules. Recently, several image-processing techniques have cooperated for the early prediction of lung nodules. However, it's hard to detect strong nodes because of lung node diversity and environmental complexity. This study presents a hybrid machine learning technique for predicting an early prognosis of lung nodules from clinical images using a learning-based neural network classifier.First, we introduce an improved snake swarm optimization with a bat model (ISSO-B) for lung nodule segmentation using statistical information. Second, we demonstrate a chaotic atom search optimization (CASO) algorithm to select the optimal best features among multiple features, which minimize the dimensionality problem. Third, we develop a hybrid learning-based deep neural network classifier (L-DNN) for nodule prediction and classification. Finally, we evaluate our proposed technique with different public datasets LIDC-IDRI and FAH-GMU. Then, performance can be compared with the latest technology in terms of accuracy, sensitivity, specificity, and area under curve (AUC).

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“…However, implementing the US and prospective lung cancer screening in Europe would likely lead to many whole-slide histopathology images, biopsies, and excised tumors. Researchers have proposed many medical image analysis methods in CT scans to segregate the lung parenchyma region automatically [ 6 ]. For example, authors in [ 7 ] describe signal thresholding strategies based on contrast information for most methods.…”

Section: Introductionmentioning

confidence: 99%

SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans

Çifçi

2022

Applied Bionics and Biomechanics

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Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.

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Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest

Cited by 23 publications

References 54 publications

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans

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