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Author Aziz Barbar Ph., D.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Communication System ♦ General Term Content ♦ Medical Image ♦ Sub Window ♦ System Implementation ♦ Different Field ♦ Indexing Random Sub Window ♦ Image Classification ♦ Simple Text ♦ Local Sub-windows ♦ Mcbir Support System ♦ Decision Support System ♦ Data Issue ♦ Digital Imaging ♦ Unique Image Processing ♦ Future Development ♦ Main Step ♦ Image Retrieval ♦ Computer Vision System ♦ Critical Deduction ♦ Practical Use ♦ Future Module ♦ Certain Medical Institute ♦ Medical Industry ♦ Medical Institute ♦ Picture Archiving ♦ Visual Data ♦ Available Data ♦ Similarity Measure ♦ Machine Learning ♦ Knowledge-based System ♦ Random Sub Window ♦ Medical Content-based Image Retrieval ♦ Decision Tree Ensemble ♦ High Cost ♦ Data Repository ♦ Generic Approach ♦ Medical Expert
Abstract The amount of multimedia and visual data generated throughout the different fields is growing rapidly; a fact that has raised the need for a method which allows searching and accessing the provided data both quickly and easily, since querying data repositories must not be based solely on simple text. This paper presents the Medical Content-Based Image Retrieval (MCBIR) that introduces a unique image processing and computer vision system to the medical industry. The MCBIR Support System introduced herein is intended to be easy and user friendly; it also avails a decision support system and a built in knowledge-based system. These sub-systems should be able to help in providing a pre-diagnosis that is to support the decision made by the medical experts. As per the system’s implementation, there are three main steps: the extraction of random sub windows from medical images; the building of the ensembles of trees from the extracted sub windows; and, the derivation of similarity measures between images and their practical use in MCBIR. The proposed solution operates on the Indexing Random Sub Windows with randomized trees and a critical deduction on scaling found in A Generic Approach for Image Classification Based on Decision Tree Ensembles and Local Sub-Windows. However, advanced MCBIR faces some limitations such as the high cost required for production, and the need of training data to be used for machine learning. A solution for training data issue can be achieved by acquiring the available data of a certain medical institute. As for future developments, future modules can be introduced to the system by linking it to the Picture Archiving and Communication System (PACS) of the medical institute or hospital. General Terms Content based image retrieval, Digital imaging and communication in medicine, Picture archiving and communication systems.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article