An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing

2013 ◽  
Vol 5 (4) ◽  
pp. 543-551 ◽  
Author(s):  
Ailing De ◽  
Chengan Guo
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Vector Quantization ◽  
Medical Image ◽  
Medical Image Processing ◽  
Segmentation Method

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.

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.

scholarly journals Nonreference Medical Image Edge Map Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Chen Gao ◽  
Sos Agaian ◽  
Shahan Nercessian
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Medical Image ◽  
Visual Analysis ◽  
Medical Image Processing ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Reconstructed Image ◽  
False Alarms ◽  
Original Image

Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

2011 ◽  
pp. 885-894
Author(s):  
Guang Li ◽  
Deborah Citrin ◽  
Robert W. Miller ◽  
Kevin Camphausen ◽  
Boris Mueller ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Information ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Emerging Technology ◽  
Time Image ◽  
Over Time

Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

2008 ◽  
pp. 1-9 ◽  
Author(s):  
Guang Li ◽  
Deborah Citrin ◽  
Robert W. Miller ◽  
Kevin Camphausen ◽  
Boris Mueller ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Registration ◽  
Medical Image ◽  
Medical Information ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Emerging Technology ◽  
Time Image ◽  
Over Time

Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

scholarly journals Image Segmentation using Various Approaches

2013 ◽  
pp. 86-95
Author(s):  
Snehal B. Ranit ◽  
Dr. Nileshsingh V. Thakur
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Euclidean Distance ◽  
Cluster Formation ◽  
Medical Image Processing ◽  
Experimental Result ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Segmentation Image

This paper addresses the issue of image segmentation. Image segmentation process is the main basic process or technique used in various image processing problem domains, for example, content based image retrieval; pattern recognition; object recognition; face recognition; medical image processing; fault detection in product industries; etc. Scope of improvement exists in the following areas: Image partitioning; color based feature; texture based feature, searching mechanism for similarity; cluster formation logic; pixel connectivity criterion; intelligent decision making for clustering; processing time; etc. This paper presents the image segmentation mechanism which addresses few of the identified areas where the scope of contribution exists. Presented work basically deals with the development of the mechanism which is divided into three parts first part focuses on the color based image segmentation using k-means clustering methodology. Second part deals with region properties based segmentation. Later, deals with the boundary based segmentation. In all these three approaches, finally the Steiner tree is created to identify the class of the region. For this purpose the Euclidean distance is used. Experimental result justifies the application of the developed mechanism for the image segmentation.

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