creators_name: T, Kesavamurthy
creators_name: S, SubhaRan
type: journale
datestamp: 2006-09-01
lastmod: 2011-03-11 08:56:34
metadata_visibility: show
title: Research
Pattern Classification using imaging techniques for Infarct and
Hemorrhage Identification in the Human Brain
ispublished: pub
subjects: calcut
full_text_status: public
keywords: Biomedical image processing, Hemorrhage, Infarct,
CT, MRI, Mammogram and lesion
abstract: Medical Image analysis and processing has great
significance in the field of medicine, especially in Non-
invasive treatment and clinical study. Medical imaging
techniques and analysis tools enable the Doctors and
Radiologists to arrive at a specific diagnosis. Medical Image
Processing has emerged as one of the most important tools
to identify as well as diagnose various disorders. Imaging
helps the Doctors to visualize and analyze the image for
understanding of abnormalities in internal structures. The
medical images data obtained from Bio-medical Devices
which use imaging techniques like Computed Tomography
(CT), Magnetic Resonance Imaging (MRI) and
Mammogram, which indicates the presence or absence of
the lesion along with the patient history, is an important
factor in the diagnosis. The algorithm proposes the use of
Digital Image processing tools for the identification of
Hemorrhage and Infarct in the human brain, by using a
semi-automatic seeded region growing algorithm for the
processing of the clinical images. The algorithm has been
extended to the Real-Time Data of CT brain images and
uses an intensity-based growing technique to identify the
infarct and hemorrhage affected area, of the brain. The
objective of this paper is to propose a seeded region
growing algorithm to assist the Radiologists in identifying
the Hemorrhage and Infarct in the human brain and to arrive
at a decision faster and accurate.¢Lp¤L
date: 2006
date_type: published
publication: Calicut Medical Journal
volume: 4
number: 3
publisher: Calicut Medical College Alumni Association
refereed: TRUE
referencetext: [1] Andy Tsai, Anthony Yezzi, Jr., William Wells, Allan Wilsky,
A Shape based approach to the Segmentation of Medical
Imagery using Level Sets, IEEE Transactions on Medical
Imaging., vol. 22, pp. 137-154, February 2003.
[2] Rishi Rakesh, Probal Chaudhuri, C.A.Murthy, Thresholding
in Edge Detection: A Statistical Approach, IEEE Transactions
on Image Processing, vol.13, pp. 927-936, July 2004.
[3] Cheng-Hung, Wen-Nung Lie, A Downstream Algorithm
based on Extended Gradient Vector flow field for Object
Segmentation, IEEE Transactions on Image Processing, vol.
13, pp. 1379-1392, October 2004.
[4] Rafael. C. Gonzalez, Richard. E. Woods, Digital Image
Processing, Addison Wesley, Massachusetts, 1999.
[5] P.Reimer, Paul M. Parizel, Clinical CT Imaging: A Practical
Approach, Springer, New York, 2004.
[6] Paul Suetens, Fundamentals of Medical Imaging, Cambridge
University Press, London, 2002.
[7] Lippincott Raven, William E. Erkonen, Wilbur, Radiology:
the Basics and Fundamentals of Imaging, Lippincott Williams
& Wilkins, New York, 1998.
[8] Division of Physiological Imaging, Department of Radiology,
University of IOWA College of medicine: dpi.radiology
.uiowa.edu / vida /image/
[9] Math works worldwide, Matlab Home, Image Processing
Toolbox, Image Processing, analysis and algorithm
development: http://www.mathworks.com/products/image
[10] DICOM Standard, An Introduction to DICOM:
http://www.psychology.nottingham.ac.uk/staff/cr1/dicom.htm
[11] Digital Image Processing Lab, Division of Basic Radiological
Sciences, Department of Radiology, University of Michigan
Health System: http://www.dipl.rad.med.umich.edu/
citation:   T, Kesavamurthy and S, SubhaRan  (2006) Research Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain.  [Journal (On-line/Unpaginated)]     
document_url: http://cogprints.org/5089/1/e1.pdf