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.

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.

A Survey of Deep Learning Based Methods in Medical Image Processing

2019 ◽  
Vol 15 ◽  
Author(s):  
Yinglei Song ◽  
Mohammad N.A. Rana ◽  
Junfeng Qu ◽  
Chunmei Liu
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Future Research ◽  
Accurate Analysis ◽  
Important Approach ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Made In

Background: Recently, deep learning based methods have become an important approach to the accurate analysis of medical images. Methods: This paper provides a comprehensive survey of the most important deep learning based methods that have been developed for medical image processing. A number of important contributions made in last five years are summarized and surveyed. Results: Specifically, deep learning based algorithms developed for image segmentation, image classification, registration, object detection and other important problems are reviewed. In addition, an overview of challenges that currently exist in the field and potential directions for future research is provided in the end of the survey.

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.

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.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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