scholarly journals High-performance method of morphological medical image processing

2016 ◽  
pp. 54-56
Author(s):  
Maxim Sergeevich Ryabykh ◽  
◽  
Ekaterina Sergeevna Soynikova ◽  
Denis Sergeevich Batishchev ◽  
◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
High Performance ◽  
Medical Image Processing

scholarly journals Medical Image Processing in an Era of High-Performance Computing

1994 ◽  
Vol 03 (01) ◽  
pp. 61-68
Author(s):  
Leiguang Gong ◽  
Casimir A. Kulikowski
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Communication Systems ◽  
Medical Image ◽  
High Performance ◽  
Medical Image Processing ◽  
Gross Anatomy ◽  
Visual Knowledge ◽  
Visual Databases ◽  
Picture Archiving And Communication

AbstractAdvanced radiology practices are already benefiting from powerful and increasingly more economical computing and networking facilities. Medical image processing methods have improved dramatically over the past five years, with sophisticated 3D display, visualization and analysis techniques allowing increased integration of multiple modalities of imaging, flexible environments for imaging analysis, and P ACS (picture archiving and communication systems) for ease of transmission and retrieval. Emerging directions involve teleradiology and telesurgery virtual reality applications, the development of new image database techniques, and the building of large visual databases like that of the Visible Human Project. Challenging problems of image segmentation, registration, and multimodal image fusion are still with us. Building dynamic, flexible electronic atlases will have a profound effect on the understanding of structure and function from the level of cellular physiology to gross anatomy, but requires the development of new techniques of visual knowledge representation and more standardized ways of defining the conceptual and linguistic constructs of visual objects in biomedicine, for linkage to medical records, research results, and educational materials. Methods for reasoning with visual information in the context of multimedia information systems present an inviting challenge to the upcoming generation of researchers in medical informatics.

scholarly journals A novel approach based on segmentation for securing medical image processing over cloud

2018 ◽  
Vol Special Issue on Scientific... ◽  
Author(s):  
Mbarek Marwan ◽  
Ali Kartit ◽  
Hassan Ouahmane
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
High Performance ◽  
Medical Image Processing ◽  
Healthcare Sector ◽  
Medical Providers ◽  
Novel Approach ◽  
Segmentation Approach ◽  
International Audience

International audience Healthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system.

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.

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