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Measure the Effectiveness of an Innovative Scheme for Medical Imaging

by Anamika Ahirwar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 37 - Number 2
Year of Publication: 2012
Authors: Anamika Ahirwar
10.5120/4577-6492

Anamika Ahirwar . Measure the Effectiveness of an Innovative Scheme for Medical Imaging. International Journal of Computer Applications. 37, 2 ( January 2012), 1-7. DOI=10.5120/4577-6492

@article{ 10.5120/4577-6492,
author = { Anamika Ahirwar },
title = { Measure the Effectiveness of an Innovative Scheme for Medical Imaging },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 37 },
number = { 2 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume37/number2/4577-6492/ },
doi = { 10.5120/4577-6492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:08.585123+05:30
%A Anamika Ahirwar
%T Measure the Effectiveness of an Innovative Scheme for Medical Imaging
%J International Journal of Computer Applications
%@ 0975-8887
%V 37
%N 2
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic segmentation of tumor (cancer) region in medical imaging is an extremely challenging task. This plays a significant role in cancer research and clinical practices. The segmentation technique is widely used by the radiologists to segment the input medical image into meaningful regions. In this research, an innovative method is proposed for segmenting medical images based on SOM neural network. Then associate semantics to these regions using fuzzy reasoning. A hypothesis is established for brain MRI images and mammographs for breast cancer. This paper divides into seven sections: First section illustrates a brief introduction about the paper. Second section describes the overview of the scheme. Third section illustrates the image pre-processing of medical diagnosis. Computation of statistical features is described in section fourth. Section fifth calculates Chi-Square values for brain MRI images and mammogram images. Scheme evaluation for brain MRI and mammogram image are described in sixth section. Finally conclude in section seven.

References
  1. The whole brain Atlas by Keith A. Johnson and J. Alex Becker.
  2. University of South Florida Digital Mammography Home Page.
  3. Anamika Ahirwar and R.S. Jadon, “Segmentation and Characterization of Brain MR Image Regions Using SOM and Neuro Fuzzy Techniques”, published in Proceedings of the First International Conference on Emerging Trends in Soft Computing and ICT (SCIT 2011), organized by Guru Ghasidas Vishwavidyalaya, Bilaspur(C.G.) India, PP 128-131,16-17 March 2011, ISBN NO.-978-81-920913-3-4.
  4. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2004-2007.
  5. U.S. Breast Cancer Statistics.
  6. “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique”, by S. Murugavalli and V. Rajamani, Journal of Computer Science 3 (11): 841-846, 2007, ISSN 1549-3636.
  7. Juha Vesanto and Esa Alhoniemi, “Clustering of the Self-Organizing Map”. IEEE Transactions on Neural Networks, 11(2):586–600, March 2000.
  8. Anamika Ahirwar and R.S. Jadon, “Tumor Region Extraction in Cancerous Brain MRI Images”, published in International Journal of Computer Information Systems(IJCIS ISSN 2229-5208), through Silicon Valley Publishers, Vol 2 No.4, PP 72-95, April 2011.
  9. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Self Organizing Map(SOM) Fuzzy C-means(FCM) Cerebrospinal Fluid(CSF) Central Brain Tumor Registry of the United States(CBTRUS) The National Program of Cancer Registries(NPCR) Surveillance Epidemiology and End Results(SEER)

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