The week's pick
Random Articles
Reseach Article
Detection and Identification of Mass Structure in Digital Mammogram
| 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
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
- American Cancer Society(2009a). Breast cancer facts and figures 2009-2010. Atlanta: American cancer society, Inc.
- N. B. C. S. P. W site. (2005). [Online]. Available at : www. cancerscreening. nhs. uk/breastscreen
- R. Zwiggelaar,T. CParr,J. E. Schumm,I. W. Hutt,C. J. Taylor,S. M. Asley and C. R. M. Boggis, "Model based detection of peculated lesions in mammograms"Med Image Anal. ,Vol. 3, no. 1,pp39-62,1999.
- E. L Thurfjell and J. A. Lindgren, "Breast cancer survival rates with mammographic screening : Similar favourable survivia rates for women younger and those older than 50 years" Radiology, vol. 201,no. 2,pp. 421-426, 1996.
- P. B. Dean,"Overview of breast cancer screening" in proc. Digital Mammography 1996. Amsterdam,The Netherlands: Elsevier,pp19-26
- I. Andersson,"Breast cancer screening results" in Proc. Digital mammography 1998. Norwell, MA: Kulwer, pp 427-433.
- J. Wei,B. Sahiner,L. Hadjiiski, H. Chan, N. Petrick, M. Helvie, M. Roubidoux, J. Ge and C. Zhou, " Computer – aided detection of breast masses on full field digital mammograms,"Med. Phys. ,vol. 32,no. 9,pp. 2827-2838,2005.
- B. Zheng,J. Leader,G. Abrams,A. Lu,L. Wallace,G. Maitz and D. Gur, "Multiview – based computer – aided detection scheme for breast masses,"Med. Phys. , vol. 33,no. 9,pp. 3135-3143,2006.
- Y. Wu,J. Wei,L. Hadjiiski,J. Wei,C. Zhou,B. Sahiner,J. Ge,J. Shi,Y. Zhang and H. Chan, "Bileteral analysis based false positive reduction for computer – aided mass detection," Med. Phys. , vol. 34,no. 8,pp. 3334-3344.
- 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
Keywords