Article thumbnail

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

By A Mohd. Khuzi, R Besar, WMD Wan Zaki and NN Ahmad

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 Az = 0.84 for Otsu’s method, 0.82 for thresholding method and Az = 0.7 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

Topics: Original Article
Publisher: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Malaysia
OAI identifier: oai:pubmedcentral.nih.gov:3097782
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (2008). A computer aided diagnosis system in mammography using artificial neural networks.
  2. (2007). A new ANN-based detection algorithm of the masses in digital mammograms.
  3. (2007). Abdel-Qader I. Detection of breast cancer using independent component analysis.
  4. (2007). American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. A Cancer Journal for Clinicians
  5. (2006). Approaches for automated detection and classification of masses in mammogram.
  6. (2007). Automated detection of masses in digital mammograms based on pyramid.
  7. (2005). Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Systems With Applications
  8. (2005). Comparison of thresholding methods for breast tumor cell segmentation.
  9. (2007). Computer aided diagnosis of digital mammograms.
  10. (2007). Contrast Limited Adaptive Histogram Equalization and 8-bit Quantization for Breast Mass Pre-processing and
  11. (2000). Detection of masses in mammograms using texture features.
  12. (2005). Diagnostic performance of digital versus film mammography for breast-cancer screening.
  13. (2007). Digital mammogram spiculated mass detection and spicule segmentation using level sets.
  14. (2007). Enhanced multi-level thresholding segmentation and rank based region selection for detection of masses in mammograms.
  15. (2004). Image enhancement for radiography inspection.
  16. (2001). Ipsilateral multi-view CAD system for mass detection in digital mammography.
  17. Mass screening and features reserved compression in a computer-aided system for mammograms.
  18. (2004). ROC Graphs: Notes and Practical Considerations for Researchers.
  19. (2008). Texture features selection for masses detection in digital mammogram.
  20. (2008). The women’s health resource, Ultrasound imaging of the breast [Online]. Available at http://www.imaginis.com/breasthealth/ultrasound.asp.
  21. (2006). Usefulness of texture analysis for computerized classification of breast lesion on mammograms.
  22. (2005). Wavelet based adaptive algorithm for mammographic images enhancement and denoising.