An Improved Watershed Segmentation Method of Medical Image

2015 ◽  
Vol 719-720 ◽  
pp. 1009-1012
Author(s):  
Yu Bin Jiao ◽  
Yan Lei Xu ◽  
Chao Feng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Result Show

The image segmentation is very important in medical image processing. The paper studies the watershed segmentation, and over-segmentation is the main problem of watershed. Based on this, the paper proposed an improved watershed medical image segmentation method. And the corresponding simulation is done and the result show that the method can resolve the over-segmentation of watershed and can achieve good segmentation.

Some Implementation of MATITK on Medical Image Processing

2013 ◽  
Vol 718-720 ◽  
pp. 2035-2039
Author(s):  
Yu Wen Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Filtering ◽  
Medical Image Segmentation ◽  
Simple Function ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Medical image processing includes many basic components such as medical image filtering, medical image segmentation and medical image registration, whose advanced algorithms can be found in ITK platform. But the ITK is difficult for the beginners. Only simple function is used to call dozens of image processing algorithms by MATITK . Therefore by using MATITK , the students can master these advanced algorithms and the improved implementation results can be obtained.

Level-set image processing methods in medical image segmentation

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcin Maciejewski ◽  
Wojciech Surtel ◽  
Barbara Maciejewska ◽  
Teresa Małecka-Massalska
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Level Set ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Processing Methods ◽  
Quality Of Results ◽  
Computed Tomography Images

AbstractIn this paper, two image processing methods for use in medical image processing based on the level set method are described. The theoretical bases are described and the methods are applied to a set of sample computed tomography images. The results are then compared. The results indicate that the Chan-Vese method is more useful for image segmentation in medicine than the distance-regulated method owing to both the significant differences in calculation time and the quality of results obtained for noisy images.

Interactive Medical Image Segmentation Method with only Positive Label Examples

2014 ◽  
Vol 989-994 ◽  
pp. 1088-1092
Author(s):  
Chen Guang Zhang ◽  
Yan Zhang ◽  
Xia Huan Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
High Quality ◽  
Neighbor Graph ◽  
Foreground Region ◽  
Training Examples ◽  
Positive Label ◽  
Negative Training

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING

Fractals ◽  
1994 ◽  
Vol 02 (03) ◽  
pp. 363-369 ◽  
Author(s):  
WALTER S. KUKLINSKI
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Fractal Dimension ◽  
Medical Image ◽  
Tissue Characterization ◽  
Optimization Technique ◽  
Medical Image Processing ◽  
Fractal Image ◽  
Segmentation Process ◽  
Image Models

One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

Level Set Segmentation Method in Medical Image Segmentation Research and Application

2012 ◽  
Vol 157-158 ◽  
pp. 1012-1015 ◽  
Author(s):  
Yu Miao ◽  
Wei Li Shi
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Level Set ◽  
Medical Image ◽  
Region Of Interest ◽  
Medical Image Segmentation ◽  
The Other ◽  
Segmentation Method ◽  
Level Set Segmentation ◽  
Mri Image

Medical image segmentation can be divided into two categories: one is the region of interest (ROI) identification; the other is the description of the integrity and the extraction of interest region. The emergence of the level set method greatly promoted the development of medical image segmentation. This paper studies three different level set segmentation algorithm to achieve the effective segmentation for brain gray matter and white matter of MRI image.

scholarly journals A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuqing Yang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods ◽  
Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

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