CT Image Segmentation Method Combining Wavelet Transform and RSF Model

Objective: We used U-shaped convolutional neural network (U_Net) multi-constraint image segmentation method to compare the diagnosis and imaging characteristics of tuberculosis and tuberculosis with lung cancer patients with Computed Tomography (CT). Methods: We selected 160 patients with tuberculosis from the severity scoring (SVR) task is provided by Image CLEF Tuberculosis 2019. According to the type of diagnosed disease, they were divided into tuberculosis combined with lung cancer group and others group, all patients were given chest CT scan, and the clinical manifestations, CT characteristics, and initial suspected diagnosis and missed diagnosis of different tumor diameters were observed and compared between the two groups. Results: There were more patients with hemoptysis and hoarseness in pulmonary tuberculosis combined with lung cancer group than in the pulmonary others group (P<0.05), and the other symptoms were not significantly different (P>0.05). Tuberculosis combined with lung cancer group had fewer signs of calcification, streak shadow, speckle shadow, and cavitation than others group; however, tuberculosis combined with lung cancer group had more patients with mass shadow, lobular sign, spines sign, burr sign and vacuole sign than others group. Conclusion: The symptoms of hemoptysis and hoarseness in pulmonary tuberculosis patients need to consider whether the disease has progressed and the possibility of lung cancer lesions. CT imaging of pulmonary tuberculosis patients with lung cancer usually shows mass shadows, lobular signs, spines signs, burr signs, and vacuoles signs. It can be used as the basis for its diagnosis. Simultaneously, the U-Net-based segmentation method can effectively segment the lung parenchymal region, and the algorithm is better than traditional algorithms. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 How to cite this:Zhu F, Zhang B. Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1705-1709. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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An Improved Image Segmentation Method Based Onmulti-Resolution Analysis and Watershed Transformation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.709 ◽

2012 ◽

Vol 500 ◽

pp. 709-715

Author(s):

Yan Wang ◽

Yan Ma

Keyword(s):

Image Segmentation ◽

Wavelet Transform ◽

Input Image ◽

Segmentation Method ◽

Watershed Segmentation ◽

Watershed Transformation ◽

Inverse Transform ◽

Multi Resolution Analysis ◽

Segmentation Accuracy ◽

Resolution Analysis

This paper presents an improved image segmentation method based on multi-resolution analysis of wavelet transform and watershed transformation. In the marked-controlled watershed segmentation, we not only enhance the contours of low-resolution input image to acquire segmentation function image, but also use minima imposition technology to apply filters to input image to acquire marked function image. In order to improve segmentation accuracy, we use regional fusion and coarse-fine segmentation in wavelet inverse transform. The experimental results show that the proposed image segmentation method can efficiently reduce over-segmentation, as well as improve the effect of image segmentation. In addition, the proposed method is robust.

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IMAGE SEGMENTATION USING NCUT IN THE WAVELET DOMAIN

International Journal of Image and Graphics ◽

10.1142/s0219467806002458 ◽

2006 ◽

Vol 06 (04) ◽

pp. 569-582 ◽

Cited By ~ 5

Author(s):

EMMA REGENTOVA ◽

DONGSHENG YAO ◽

SHAHRAM LATIFI ◽

JUN ZHENG

Keyword(s):

Image Segmentation ◽

Wavelet Transform ◽

Graph Partitioning ◽

Perceptual Grouping ◽

Image Features ◽

Discrete Wavelet ◽

Wavelet Domain ◽

Segmentation Method ◽

Normalized Cuts ◽

Grouping Problem

A new image segmentation method is developed that combines the advantage of the normalized cuts (Ncut) algorithm to solve the perceptual grouping problem by means of graph partitioning, and the ability of wavelet transform to capture image features by decomposing signal both in time and frequency. We derive image features from orientation histograms defined on the detail subbands of the discrete wavelet transform. The segmentation is implemented by partitioning a graph representing an image at the coarsest transform level, while the weights of the graph are calculated from all the scales. Due to the reduced dimensionality of the dataset, the speed of Ncut is increased. Even though segmentation is carried out at a coarsest level of transform, the results are accurate for images of different structural contents, including textures.

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CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging

Mathematical Problems in Engineering ◽

10.1155/2021/9919507 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Liping Liu ◽

Lin Wang ◽

Dan Xu ◽

Hongjie Zhang ◽

Ashutosh Sharma ◽

...

Keyword(s):

Artificial Intelligence ◽

Image Segmentation ◽

Medical Imaging ◽

Liver Tumor ◽

Blood Type ◽

Arterial Phase ◽

Low Density ◽

Ct Image ◽

Segmentation Method ◽

Deposition Effect

Artificial intelligence (AI) has made various developments in the image segmentation techniques in the field of medical imaging. This article presents a liver tumor CT image segmentation method based on AI medical imaging-based technology. This study proposed an artificial intelligence-based K-means clustering (KMC) algorithm which is further compared with the region growing (RG) method. In this study, 120 patients with liver tumors in the Post Graduate Institute of Medical Education & Research Hospital, Chandigarh, India, were selected as the research objects, and they were classified according to liver function (Child–Pugh), with 58 cases in grade A and 62 cases in grade B. The experimentation indicates that liver tumor showed low density on plain CT scan, moderate enhancement in the arterial phase of the enhanced scan, and low-density filling defect in the involved blood vessel in the portal venous phase (PVP). It was observed that the CT examination is more sensitive to liver metastasis than hepatocellular carcinoma ( P < 0.05 ). The outcomes obtained depict the good deposition effect of lipiodol chemotherapy emulsion (LCTE) in the contrast group with rich blood type accounted for 53.14% and the patients with the poor blood type accounted for 25.73% showed poor deposition effect. The comparison with the state-of-the-art method reveals that the segmentation effect of the KMC algorithm is better than that of the conventional RG method.

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