scholarly journals Three-Dimensional Modeling in Medical Image Processing by Using Fractal Geometry

2017 ◽  
pp. 479-485
Author(s):  
Abidin Çalışkan
Keyword(s):  
Image Processing ◽  
Fractal Geometry ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Three Dimensional Modeling ◽  
Dimensional Modeling

scholarly journals IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW

2017 ◽  
Vol 79 (7) ◽  
Author(s):  
Azlan Muharam ◽  
Afandi Ahmad
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Medical Image ◽  
Rapid Development ◽  
Three Dimensional ◽  
Medical Image Processing ◽  
Subjective Test ◽  
Medical Image Compression ◽  
Research Issues ◽  
Software And Hardware

The rapid development of medical imaging and the invention of various medicines have benefited mankind and the whole community. Medical image processing is a niche area concerned with the operations and processes of generating images of the human body for clinical purposes.  Potential areas such as image acquisition, image enhancement, image compression and storage, and image based visualization also include in medical image processing analysis. Unfortunately, medical image compression dealing with three-dimensional (3-D) modalities still in the pre-matured stage. Along with that, very limited researchers take a challenge to apply hardware on their implementation. Referring to the previous work reviewed, most of the compression method used lossless rather than lossy. For implementation using software, MATLAB and Verilog are the famous candidates among researchers. In term of analysis, most of the previous works conducted objective test compared with subjective test. This paper thoroughly reviews the recent advances in medical image compression mainly in terms of types of compression, software and hardware implementations and performance evaluation. Furthermore, challenges and open research issues are discussed in order to provide perspectives for future potential research. In conclusion, the overall picture of the image processing landscape, where several researchers more focused on software implementations and various combinations of software and hardware implementation.  

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.

Analysis of Medical Images Using Fractal Geometry

2019 ◽  
pp. 181-201 ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Jibitesh Mishra
Keyword(s):  
Image Processing ◽  
Fractal Geometry ◽  
Medical Image ◽  
Vascular System ◽  
Medical Image Analysis ◽  
Medical Image Processing ◽  
Research Area ◽  
Self Similarity ◽  
Complex Objects ◽  
Box Counting

Fractal dimension is an emerging research area in order to characterize the complex or irritated objects found in nature. These complex objects are failed to analyze by classical Euclidian geometry. The concept of FD has extensively applied in many areas of application in image processing. The thought of the FD will work based upon the theory of self-similarity because it holds structures that are nested with one another. Over the last years, fractal geometry was applied extensively in medical image analysis in order to detect cancer cells in human body because our vascular system, nervous system, bones, and breast tissue are so complex and irregular in pattern, and also successfully applied in ECG signal, brain imaging for tumor detection, trabeculation analysis, etc. In order to analyze these complex structures, most of the researchers are adopting the concept of fractal geometry by means of box counting technique. This chapter presents an overview of box counting and its improved algorithms and how they work and their application in the field of medical image processing.

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.

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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