Advances in Biomedical Image Analysis

2004 ◽  
Vol 43 (04) ◽  
pp. 308-314 ◽  
Author(s):  
H. P. Meinzer ◽  
T. Tolxdorff ◽  
T. M. Lehmann
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Imaging Modality ◽  
Image Interpretation ◽  
Medical Image Processing ◽  
Biomedical Image Processing ◽  
Technical Parameters ◽  
Biomedical Image ◽  
Data Files ◽  
Task Oriented

SummaryStarting from raw data files coding eight bits of gray values per image pixel and identified with no more than eight characters to refer to the patient, the study, and technical parameters of the imaging modality, biomedical imaging has undergone manifold and rapid developments. Today, rather complex protocols such as Digital Imaging and Communications in Medicine (DICOM) are used to handle medical images. Most restrictions to image formation, visualization, storage and transfer have basically been solved and image interpretation now sets the focus of research. Currently, a method-driven modeling approach dominates the field of biomedical image processing, as algorithms for registration, segmentation, classification and measurements are developed on a methodological level. However, a further metamorphosis of paradigms has already started. The future of medical image processing is seen in task-oriented solutions integrated into diagnosis, intervention planning, therapy and followup studies. This alteration of paradigms is also reflected in the literature. As German activities are strongly tied to the international research, this change of paradigm is demonstrated by selected papers from the German annual workshop on medical image processing collected in this special issue.

Biomedical Image Processing Overview

2016 ◽  
pp. 1-12 ◽  
Author(s):  
Monia Mannai Mannai ◽  
Wahiba Ben Abdessalem Karâa
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image Processing ◽  
Main Process ◽  
Nuclear Medicine Imaging ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
X Ray Computed ◽  
Magnetic Resonance Imaging Mri ◽  
High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

Development of Biomedical Image Processing Software Package for New Learners

2013 ◽  
Vol 61 (2) ◽  
Author(s):  
Nasrul Humaimi Mahmood ◽  
Lee Chen Hsieng ◽  
Siti Asmah Daud
Keyword(s):  
Image Processing ◽  
Software Package ◽  
Medical Image ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
The Matrix ◽  
Live Demonstration ◽  
Mathematical Equations ◽  
Processing Techniques ◽  
User Friendly

Biomedical image processing techniques involve a lot of mathematical equations and new learners need to calculate manually in order to analyze such techniques. It is therefore very important that they understand the biomedical image processing through live demonstration to add to their understanding of the techniques. This research will enable the biomedical students and staff, who are new learners in medical image processing, to use a software package that can analyze various techniques of biomedical image processing. It is one of the alternative ways to improve their learning process. Moreover, developing this user friendly software package using the Matrix Laboratory (MATLAB) will bring a lot of benefits to the users. Among the benefits of the software are shortening the time to process an image and helping the new learners to study about image processing because they can observe the particular object rather than just applying mathematical equations.

Biomedical Image Processing Overview

2017 ◽  
pp. 59-70
Author(s):  
Monia Mannai Mannai ◽  
Wahiba Ben Abdessalem Karâa
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image Processing ◽  
Main Process ◽  
Nuclear Medicine Imaging ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
X Ray Computed ◽  
Magnetic Resonance Imaging Mri ◽  
High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

scholarly journals Challenges of Medical Text and Image Processing

2019 ◽  
Vol 8 (6S2) ◽  
pp. 969-973
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Research Work ◽  
Medical Image Processing ◽  
Medical Application ◽  
Medical Sciences ◽  
Text And Image ◽  
Biomedical Image ◽  
Day By Day ◽  
Made In

The processing of medical image has encountered sensational development, and its researching is doing by almost every field of engineering or medical sciences like designing, insights, material science, science and medication. CADxprocessing has recently transformed into a critical bit of clinical day by day practice. Joined by a flood of new headway of high advancement and use of various imaging modalities, more challenges develop; for example, how to process and separate a basic volume of images with the objective that splendid information can be conveyed for ailment ends and treatment. Thusly, the inside steps of image examination, to be explicit: include extraction, division, order, quantitative estimations, and translation are displayed in sperate portions. Because of its high importance, the attention is on division of biomedical image. Remarkable division methodologies and strategies have been made in the medical application zone. The principle goals of this paper are to give a preamble to crucial thoughts and frameworks for medicinal image taking care of and to propel interests for further assessment and research in medical imaging handling. We will display the Medical Image Processing and layout related research work around there.

scholarly journals A REVIEW PAPER ON MEDICAL IMAGE PROCESSING

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 21-29
Author(s):  
Shruthishree ◽  
Harshvardhan Tiwari
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Applied Mathematics ◽  
Correct Diagnosis ◽  
Medical Science ◽  
Medical Image Processing ◽  
Research Field ◽  
The Body ◽  
Biomedical Image Processing

Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and treatment. The principal objectives of this course are to provide an introduction to basic concepts and techniques for medical image processing and to promote interests for further study and research in medical imaging processing.The rapid progress of medical science and the invention of various medicines have benefited mankind and the whole civilization. Modern science also has been doing wonders in the surgical field. But, the proper and correct diagnosis of diseases is the primary necessity before the treatment. The more sophisticate the bio-instruments are, better diagnosis will be possible. The medical image plays an important role in clinical diagnosis and therapy of doctor and teaching and researching etc. Medical imaging is often thought of as a way to represent anatomical structures of the body with the help of X-ray computed tomography and magnetic resonance imaging. But often it is more useful for physiologic function rather than anatomy. With the growth of computer and image technology medical imaging has greatly influenced medical field. As the quality of medical imaging affects diagnosis the medical image processing has become a hotspot and the clinical applications wanting to store and retrieve images for future purpose needs some convenient process to store those images in details.

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
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