COTS-Like Generic Medical Image Repository

2006 ◽  
Author(s):  
N. Chandrashekar ◽  
S.M. Gautam ◽  
K.R. Shivakumar ◽  
K.S. Srinivas ◽  
J. Vijayananda
Keyword(s):  
Medical Image ◽  
Image Repository

scholarly journals An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

2019 ◽  
Vol 8 (5S3) ◽  
pp. 239-244
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Low Cost ◽  
Image Features ◽  
Semantic Gap ◽  
Medical Image Retrieval ◽  
Large Size ◽  
Image Repository ◽  
High Level ◽  
Technology Growth

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

A cloud-based medical image repository

2012 ◽  
Author(s):  
Anthony J. Maeder ◽  
Birgit M. Planitz ◽  
Diaa El Rifai
Keyword(s):  
Medical Image ◽  
Image Repository

DICOM-based medical image repository using DSpace

2020 ◽  
Vol 39 (4) ◽  
pp. 105-115 ◽  
Author(s):  
Hirak Jyoti Hazarika ◽  
Akash Handique ◽  
S. Ravikumar S. Ravikumar
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Medical Image ◽  
Medical Professional ◽  
Library Science ◽  
Medical Professionals ◽  
Dicom Image ◽  
Science Field ◽  
Content Type ◽  
Image Repository

Purpose This paper aims to provide image repository to the medical professional in an open source platform, which will increase the visibility of Digital Imaging and Communication in Medicine (DICOM) image in a network mode; further, the proposed system will reduce the storage cost of the images to significant level. Design/methodology/approach The authors have developed a new institutional repository model for the medical professionals cum radiologists to preserve, store and retrieve medical images from one database with the help of open source software. The authors used JavaScript programming to integrate and develop the DICOM Standard with DSpace. Findings Major outcome of this work is that DICOM images can be accommodated in DSpace without modifying the image properties and keeping intact the various dimensions of image viewing options. Further, it was found that the images are retrieved without any ease because of the robust indexing system. Research limitations/implications Major limitation of this study was the size of the data (5000 DICOM image) with which the authors have tested the system. The scalability of the system has to be tested on various fronts, for which separate study has to be done. Practical implications Once this system is in place, DICOM user can store, retrieve and access the image from Web platform. This proposed repository will be the storehouse of various DICOM images with reasonable storage costs. Originality/value In addition to exploring the opportunities of open source software (OSS) implementation in Medical Fields, this study includes issues related to implementation of open source repository for storing and preserving medical image. This is the first time in Library Science field to create and develop Open Source DICOM Medical Image Library with the help of DSpace. The study will create value for library professionals as well as medical professionals and OSS vendors to understand the medical market in the context of OSS.

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.

A parallel segmentation method of medical image based on cluster system

2014 ◽  
Author(s):  
Yanjun Peng ◽  
Yuanhong Wang
Keyword(s):  
Medical Image ◽  
Cluster System ◽  
Segmentation Method

Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm

2018 ◽  
Vol 6 (1) ◽  
pp. 18-23 ◽  
Author(s):  
T.Gopi Krishna ◽  
◽  
K.V.N. Sunitha ◽  
S. Mishra ◽  
◽  
...  
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Medical Image

A Survey of Emerging Trends on Medical Image Enhancement Techniques

2018 ◽  
Vol 6 (9) ◽  
pp. 627-634
Author(s):  
Vijay Panse ◽  
Rajendra Gupta
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Emerging Trends ◽  
Medical Image Enhancement
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