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Detection and Identification of Mass Structure in Digital Mammogram

by Prakash Bethapudi, E. Sreenivasa Reddy, Y. Srinivas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 14
Year of Publication: 2013
Authors: Prakash Bethapudi, E. Sreenivasa Reddy, Y. Srinivas
10.5120/13591-1359

Prakash Bethapudi, E. Sreenivasa Reddy, Y. Srinivas . Detection and Identification of Mass Structure in Digital Mammogram. International Journal of Computer Applications. 78, 14 ( September 2013), 17-20. DOI=10.5120/13591-1359

@article{ 10.5120/13591-1359,
author = { Prakash Bethapudi, E. Sreenivasa Reddy, Y. Srinivas },
title = { Detection and Identification of Mass Structure in Digital Mammogram },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number14/13591-1359/ },
doi = { 10.5120/13591-1359 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:34.322037+05:30
%A Prakash Bethapudi
%A E. Sreenivasa Reddy
%A Y. Srinivas
%T Detection and Identification of Mass Structure in Digital Mammogram
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 14
%P 17-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast Cancer is the rampant issue facing by most of the women these days. Mammography is the most successful modus operandi for verdict of breast cancer. Radiologists view mammograms to perceive the abnormalities. In this paper, we urbanized an algorithm to isolate and extract the malignant masses in mammograms for detection of breast cancer. This exertion is based on the following course of action: (a) Confiscate the background information from the DICOM image. (b)Refurbish the RGB image to gray image (c) Apply thresholding to remove the background information (ROI). (d)Apply median filter on the image to reduce random noise and preserve the edges. (e)Extract the binary image contours. (f)Carry out Area-open on the image and. (g)To end with act upon filling on the resultant image. This method was tested over the real time images of various patients taken from a cancer hospital and implemented using Matlab code. Thus, capable in executing the pre-processed image efficiently and detected the segmentation region which helped in retrieving the mass present in the digital mammogram and hence documented the malignant mass by calculating different features like Area, perimeter, Compactness, Width, Height, Isotropic factor and Eccentricity of the extracted mass.

References
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  10. Prakash Bethapudi, Dr. E. Srinivasa Reddy, Dr. Madhuri. P, "Detection of Malignancy in digital mammograms form segmented breast region using morphological techniques,"IOSR-JEEE,vol. 5,Issue. 4, pp. 09-12,2013.
Index Terms

Computer Science
Information Sciences

Keywords

Contour Compactness Eccentricity Height Isotropic Malignancy Mammogram Mass Perimeter ROI Segmentation Thresholding Width.