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.

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.

Utilization Of Spatial Self-Similarity In Medical Image Processing

1987 ◽  
Author(s):  
Walter S. Kuklinski ◽  
Samuel J. Dwyer III ◽  
Roger H. Schneider
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Self Similarity

Fuzzy-Based Medical Image Processing

2015 ◽  
pp. 45-61 ◽  
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.

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.

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

Recent advances in medical image processing for the evaluation of chronic kidney disease

2021 ◽  
Vol 69 ◽  
pp. 101960
Author(s):  
Israa Alnazer ◽  
Pascal Bourdon ◽  
Thierry Urruty ◽  
Omar Falou ◽  
Mohamad Khalil ◽  
...  
Keyword(s):  
Image Processing ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Image ◽  
Medical Image Processing ◽  
Recent Advances

Research on nano-CT medical image processing based on embedded system

2021 ◽  
Vol 82 ◽  
pp. 103755
Author(s):  
Shengyan Cai ◽  
Fangyuan Chai ◽  
Chunhuan Hu ◽  
Xue Han ◽  
Shuyu Liu
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
Medical Image ◽  
Medical Image Processing
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