Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

Suji, R. Jenkin; Bhadauria, Sarita Singh; Dhar, Joydip; Godfrey, W. Wilfred

doi:10.1007/s10278-020-00346-w

Cited by 23 publications

(4 citation statements)

References 49 publications

(53 reference statements)

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“…One common intensity-based detection method is the use of a threshold to define a nodule region of interest. The threshold is typically selected manually or automatically using a training set of images [11]. To further segment the nodule region from the surrounding lung parenchyma, region-growing or watershed segmentation methods are used.…”

Section: Literature Reviewmentioning

confidence: 99%

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Rashid,

Jaffar,

Rashid

et al. 2024

CMC

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Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3-30 mm.

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Section: Literature Reviewmentioning

confidence: 99%

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Rashid,

Jaffar,

Rashid

et al. 2024

CMC

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“…At present, deep learning has been widely used in the field of computer vision. 4 Wang et al 5,6 used the deep learning method to diagnose Covid-19 and achieved good results. Long et al 7 proposed a fully convolutional network, which replaces the fully connected layers in a Convolutional Neural Network 8 (CNN) with convolutional layers to obtain the classification results of each pixel in an image, and finally achieves image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…At present, deep learning has been widely used in the field of computer vision 4 . Wang et al 5,6 used the deep learning method to diagnose Covid‐19 and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

CA‐UNet: Convolution and attention fusion for lung nodule segmentation

Wang

et al. 2023

Int J Imaging Syst Tech

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Lung cancer is one of the deadliest cancers in the world and is a serious threat to human life. Lung nodules are an early manifestation of lung cancer, early detection and treatment of which can improve the survival rate of patients. In order to accurately segment the lung nodule regions in lung CT images, CA‐UNet, an encoding and decoding structure based on convolution and attention fusion, is proposed based on the U‐Net network. It has improved on two points: First, at the skip connection, the global feature information is extracted using the Swin Transformer block and then fused with the pre‐extraction features and subsequently fed into the corresponding layer of the decoder; second, each channel information is reweighted in the decoder by the channel attention module so that the network focuses on more important channels. Experimental results on the LIDC‐IDRI public database of lung nodules showed that the intersection of union, dice similarity coefficient, precision, and recall of the algorithm were 82.42%, 89.86%, 89.07%, and 92.44%, respectively. The algorithm has better segmentation performance compared to other segmentation methods.

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“…Automatic investigation of lung CT images is necessary to calculate lung nodule characteristics to recognize malignancy [10]. The lung nodule segmentation determines the malignancy by investigating the nodule size and structure [11]. Other nodule segmentations have been represented in previous years and their accuracy is not high because of various challenges in lung nodule segmentation [12].…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation Using Cat Swarm Optimization Based Recurrent Neural Network

2023

IJIES

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Nodule segmentation in lung computed tomography (CT) images is a significant part of the detection and diagnosis of lung cancer. Automatic analysis of lung CT images is necessary to calculate lung nodule characteristics for recognizing malignancy. In recent years, deep learning and neural networks have been used in medical applications. Deep learning utilizes neural networks to train huge amounts of information which effectively learns the nodule features in lower to higher grades for segmenting and predicting the medical images. In this paper, the proposed cat swarm optimization (CSO) based recurrent neural network (RNN) is utilized for lung nodule segmentation. The proposed model is estimated on the freely accessible lung image database consortium and image database resource initiative (LIDC-IDRI) dataset. The proposed model is segmented by using the markov random field (MRF) based firefly algorithm (FA) and cuckoo search algorithm (CSA). The result shows that the proposed CSO based RNN model delivers performance metrics like dice coefficient (DC) loss and accuracy values of about 96.28% and 90.28% respectively, which ensures accurate nodule segmentation in lung CT images.

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Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

Cited by 23 publications

References 49 publications

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

CA‐UNet: Convolution and attention fusion for lung nodule segmentation

Lung Nodule Segmentation Using Cat Swarm Optimization Based Recurrent Neural Network

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