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 IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

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.

Fuzzy-Based Medical Image Processing

2015 ◽  
pp. 666-681
Author(s):  
G. R. Sinha
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Hybrid Approach ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Clustering Methods ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.

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 A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images

2010 ◽  
Vol 3 (1) ◽  
pp. 81 ◽  
Author(s):  
M. A. Yousuf ◽  
M. N. Nobi
Keyword(s):  
Image Processing ◽  
Magnetic Resonance ◽  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Medical Image Processing ◽  
Experimental Results ◽  
Signal To Noise

In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms.Key words: Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5544                J. Sci. Res. 3 (1), 81-89 (2011)

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.

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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