Identification of masses in digital mammogram using gray level co-occurrence matrices

Mohd. Khuzi, A and Besar, R and Wan Zaki, WMD and Ahmad, NN (2009) Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomedical Imaging and Intervention Journal, 5 (3). pp. 1-13. ISSN 1823-5530

[img] PDF
18.pdf
Restricted to Repository staff only

Download (0B)
Official URL: http://dx.doi.org/10.2349/biij.5.3.e17

Abstract

Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses. In this study normal breast images and breast image with masses used as the standard input to the proposed system are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. In MIAS database, masses are grouped into either spiculated, circumscribed or ill-defined. Additional information includes location of masses centres and radius of masses. The extraction of the textural features of ROIs is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions for each ROI. The results show that the GLCM at 0º, 45º, 90º and 135º with a block size of 8X8 give significant texture information to identify between masses and non-masses tissues. Analysis of GLCM properties i.e. contrast, energy and homogeneity resulted in receiver operating characteristics (ROC) curve area of <sub><img height="16" width="58" src="http://www.biij.org/2009/3/e17/image001.gif" alt="" /></sub> for Otsu’s method, 0.82 for thresholding method and <sub><img height="16" width="50" src="http://www.biij.org/2009/3/e17/image002.gif" alt="" /></sub>for K-mean clustering. ROC curve area of 0.8-0.9 is rated as good results. The authors’ proposed method contains no complicated algorithm. The detection is based on a decision tree with five criterions to be analysed. This simplicity leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis system

Item Type: Article
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 04 Feb 2013 04:19
Last Modified: 04 Feb 2013 04:19
URI: http://shdl.mmu.edu.my/id/eprint/3799

Actions (login required)

View Item View Item