scholarly journals An Approach for Pulmonary Vascular Extraction from Chest CT Images

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Wenjun Tan ◽  
Yue Yuan ◽  
Anning Chen ◽  
Lin Mao ◽  
Yuqian Ke ◽  
...  
Keyword(s):  
Region Growing ◽  
Ground Truth ◽  
Ct Images ◽  
Chest Ct ◽  
Morphological Operations ◽  
Lung Region ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Average Accuracy ◽  
Seed Points

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.

Open Source Software Implementation of Anatomical Segmentation

2018 ◽  
Author(s):  
Maggie Hess
Keyword(s):  
Large Scale ◽  
Focused Ultrasound ◽  
Region Growing ◽  
Ground Truth ◽  
Clot Lysis ◽  
Focused Ultrasound Surgery ◽  
Manual Methods ◽  
Areas Of Interest ◽  
Seed Points ◽  
Viable Method

Purpose: Intraventricular hemorrhage (IVH) affects nearly 15% of preterm infants. It can lead to ventricular dilation and cognitive impairment. To ablate IVH clots, MR-guided focused ultrasound surgery (MRgFUS) is investigated. This procedure requires accurate, fast and consistent quantification of ventricle and clot volumes. Methods: We developed a semi-autonomous segmentation (SAS) algorithm for measuring changes in the ventricle and clot volumes. Images are normalized, and then ventricle and clot masks are registered to the images. Voxels of the registered masks and voxels obtained by thresholding the normalized images are used as seed points for competitive region growing, which provides the final segmentation. The user selects the areas of interest for correspondence after thresholding and these selections are the final seeds for region growing. SAS was evaluated on an IVH porcine model.  Results: SAS was compared to ground truth manual segmentation (MS) for accuracy, efficiency, and consistency. Accuracy was determined by comparing clot and ventricle volumes produced by SAS and MS. In Two-One-Sided Test, SAS and MS were found to be significantly equivalent (p < 0.01). SAS on average was found to be 15 times faster than MS (p < 0.01). Consistency was determined by repeated segmentation of the same image by both SAS and manual methods, SAS being significantly more consistent than MS (p < 0.05).  Conclusion: SAS is a viable method to quantify the IVH clot and the lateral brain ventricles and it is serving in a large- scale porcine study of MRgFUS treatment of IVH clot lysis.

scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

Adaptive fast marching method for automatic liver segmentation from CT images

2013 ◽  
Vol 40 (9) ◽  
pp. 091917 ◽  
Author(s):  
Xiao Song ◽  
Ming Cheng ◽  
Boliang Wang ◽  
Shaohui Huang ◽  
Xiaoyang Huang ◽  
...  
Keyword(s):  
Ct Images ◽  
Fast Marching Method ◽  
Liver Segmentation ◽  
Fast Marching ◽  
Marching Method

Lymph node segmentation from CT images using fast marching method

2004 ◽  
Vol 28 (1-2) ◽  
pp. 33-38 ◽  
Author(s):  
Jiayong Yan ◽  
Tian-ge Zhuang ◽  
Binsheng Zhao ◽  
Lawrence H. Schwartz
Keyword(s):  
Lymph Node ◽  
Ct Images ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Marching Method

scholarly journals Using Distance Measure based Classification in Automatic Extraction of Lungs Cancer Nodules for Computer Aided Diagnosis

2021 ◽  
Vol 12 (3) ◽  
pp. 25-43
Author(s):  
Maan Ammar ◽  
Muhammad Shamdeen ◽  
Mazen Kasedeh ◽  
Kinan Mansour ◽  
Waad Ammar
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Ct Images ◽  
Chest Ct ◽  
Connected Components ◽  
Automatic Extraction ◽  
Morphological Operations ◽  
Connected Component ◽  
Overall Performance ◽  
Aided Diagnosis

We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying lungs connected components into nodule and not-nodule. We explain also using Connected Component Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some morphological operations. Our tests have shown that the performance of the introduce method is high. Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we tested the method by some images of healthy persons and demonstrated that the overall performance of the method is satisfactory.

scholarly journals Segmentation of Lung Images using Region based Neural Networks

2018 ◽  
Vol 11 (4) ◽  
pp. 2037-2042 ◽  
Author(s):  
Z. Faizal Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Tomography ◽  
Region Growing ◽  
Ct Images ◽  
Second Step ◽  
Lung Region ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Lung Ct

In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

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