Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

Qiu, Shi; Sun, Jianyong; Zhou, Tao; Gao, Guilong; He, Zhenan; Liang, Ting

doi:10.1155/2020/6619076

Cited by 10 publications

(6 citation statements)

References 46 publications

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“…It is common to screen and diagnose pulmonary nodules by imaging methods [ 17 , 18 ]. The chest plain CT images can clearly distinguish and judge pulmonary nodules' size, position, morphology, and internal characteristics [ 19 ]. It is of great significance to distinguish the clinic's benign and malignant pulmonary nodules.…”

Section: Discussionmentioning

confidence: 99%

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Long

2022

Computational and Mathematical Methods in Medicine

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Background and Objective. Effective segmentation of pulmonary nodules can effectively assist in the diagnosis of benign and malignant pulmonary nodules. We aim to explore the effectiveness of classification and segmentation algorithms in diagnosing benign and malignant pulmonary nodules under different CT reconstructions. Methods. The imaging data of 55 patients with chest CT plain scan in the Xuancheng People’s Hospital were collected retrospectively. The data of each patient included lung window reconstruction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance. Results. Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were 80.32 % ± 5.91 % , 79.83 % ± 6.12 % , and 80.17 % ± 5.89 % , respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all P > 0.05 ). Conclusion. The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.

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Section: Discussionmentioning

confidence: 99%

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Long

2022

Computational and Mathematical Methods in Medicine

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“…It is determined by the change of profle curve X and Y. A discretization operation on equation ( 7) yields equation (9).…”

Section: Detection Methods Of the Fuzzy Filter Bar Roundness Imagementioning

confidence: 99%

“…Tchoketch Kebir et al proposed a complete and fully automated MRI brain tumor detection and segmentation method by using active contour, wavelet transform, etc., as an efective clinical aid, presenting fast, accurate, efective and fully automated results without any human intervention as well as prior knowledge in the training phase [8]. Qiu et al team extracted the complete boundary of lung nodules by using an improved snake model and made full use of the algorithm to build maximum density based on the physician's diagnosis process of lung nodules projection model to construct a new neural network structure [9]. Guo et al developed an automatic liver segmentation method based on a new framework that uses structured network results to defne the external binding force of the activity contour model.…”

Section: Related Workmentioning

confidence: 99%

Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

Zhao

Zhang

et al. 2022

Advances in Materials Science and Engineering

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Cigarette filter rods are a kind of easily deformed and irregularly shaped parts, and the quality of cigarette filter rods is often measured by the filter rod roundness index, although they are affected by factors such as environment and humidity, which can affect the detection of filter rod roundness and show blurred images. For the detection of blurred filter rod image roundness, an improved active contour snake model algorithm is proposed. The experimental results show that in the analysis of the algorithm iteration number on the model performance, the model performance gradually leveled off at the iteration number of 200. Comparing the PR of the improved algorithm with the traditional snake algorithm with the balloon force snake algorithm, the accuracy and recall rates obtained were 0.803 and 0.798, respectively, which were greater than the other two algorithms; the error of the value of the residual under the improved snake algorithm was always smaller than the other algorithms, which indicated that the convergence effect of the improved snake algorithm on the error of filter bar roundness was better than the other experimental algorithms. The abovementioned results indicate that the improved snake algorithm has high feasibility and effectiveness in the application of filter bar roundness error detection and has a significant value.

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“…Qiu et al. [39] proposed a lung nodule spiculation recognition model, which was able to accurately extract the boundaries of lung nodules and effectively improve the recognition rate of spiculation, but did not further quantify the spiculation. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…However, the method only classifies the nodule as spiculation or non-spiculation and does not quantify and grade the spiculation. Qiu et al [39] proposed a lung nodule spiculation recognition model, which was able to accurately extract the boundaries of lung nodules and effectively improve the recognition rate of spiculation, but did not further quantify the spiculation. Wang et al [40] extracted the boundary normal-gradient orthogonal index as a quantitative index of the spiculation of lung nodules, and then quantified and graded the spiculation length.…”

Section: Related Workmentioning

confidence: 99%

A quantitative evaluation of lung nodule spiculation based on image enhancement

Tao

Wang

Ding

2022

IET Image Processing

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Lung cancer manifests itself as lung nodules at an early stage. Segmentation of lung nodules and quantitative evaluation of spiculation can assist physicians in distinguishing benign and malignant lung nodules. The identification of malignant nodules for early diagnosis and treatment can improve the survival rate of patients. In this paper, a quantitative evaluation method of lung nodule spiculation is proposed based on image enhancement. The proposed method combines super-resolution reconstruction-based image enhancement techniques with lung nodule segmentation algorithms to improve the accuracy of segmentation and to quantify the degree of distinctness of the spiculation of lung nodule, which can provide a reliable basis for computer-aided diagnosis and treatment of lung nodules. First, the method uses Laplacian pyramid image restoration technique and Gaussian differential scale-invariant feature to enhance the details and edge information of lung CT images. Then the improved Random Walk algorithm is used to segment the enhanced lung images and extract lung nodules. Finally, the spiculation index, which measures the spiculation of breast nodules, is used to quantify the spiculation of nodules. The experimental results show that the method can effectively segment lung nodules and quantitatively evaluate the spiculation of lung nodules.

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Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

Cited by 10 publications

References 46 publications

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

A quantitative evaluation of lung nodule spiculation based on image enhancement

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