IJSDR
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

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Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: Medical Image Analysis for Bone Fracture Detection Theoretical Proposal using Machine Learning
Authors Name: Mr. K. Senthil Kumar , Mrs. P. Punitha , Mr. Varun Krishnan , Mr. Rachit Sehgal
Unique Id: IJSDR1606006
Published In: Volume 1 Issue 6, June-2016
Abstract: Static image analysis is an important concept in computer vision. In medical Image processing identifying bone fractures from the given image is an important task. The proposed method is used find bone fractures in human beings. The technique is robust enough to detect both major and hairline fractures. This is achieved by combining Canny Edge Detector and the Hough Transform method. A theoretical proposal includes using machine learning to make the software intelligent. Presently in the literature people use stochastic grammar for representation where probability for each production rule is defined which in turn will set a weight-age for selection by using Bayesian likelihood estimation. For learning purpose we can use unsupervised learning, which makes use of algorithms that draw inferences from data-sets consisting of input data without labelled responses. The proposed paper aims to perform image analysis in order to parse a larger number of images of varying complexities as efficiently as possible, where efficiency pertains to speed of analysing the image and accuracy of analysing the image. Digital image analysis performs image analysis in a static fashion, i.e. it has a fixed rate of analysing the image irrespective of how many ever times you analyse the same image or another image, there is no way to improve the scheme. In contrast, unsupervised learning starts the analysis at a slow rate, but with passage of images its efficiency improves as it learns how to analyse better. Added to this is the added advantage of intelligence, hence as earlier mentioned the system learns ensuring no human interference.
Keywords: Stochastic Grammar, And-Or Graph, Bayesian Estimation, Canny-Edge Detector, Hough Transform, Gaussian filter
Cite Article: "Medical Image Analysis for Bone Fracture Detection Theoretical Proposal using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 6, page no.28 - 31, June-2016, Available :http://www.ijsdr.org/papers/IJSDR1606006.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR1606006
Registration ID:160441
Published In: Volume 1 Issue 6, June-2016
DOI (Digital Object Identifier):
Page No: 28 - 31
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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