Breast cancer detection using combined curvelet based enhancement and a novel segmentation methods

Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2015 Mar;159(1):83-6. doi: 10.5507/bp.2013.097. Epub 2014 Jan 23.


Aim: This paper describes the digital implementation of a mathematical transform namely 2D Fast Discrete Curvelet Transform (FDCT) via UnequiSpaced Fast Fourier Transform (USFFT) in combination with the novel segmentation method for effective detection of breast cancer.

Methods: USFFT performs exact reconstructions with high image clarity. Radon, ridgelet and Cartesian filters are included in this method. Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were calculated for the image and the resulting value showed that the proposed method performs well on mammogram image in reducing noise with good extraction of edges. This work includes a novel segmentation method, which combines Modified Local Range Modification (MLRM) and Laplacian of Gaussian (LoG) edge detection method to segment the textured features in the mammogram image.

Results: The result was analyzed using a Receiver Operating Characteristics (ROC) plot and the detection accuracy found was 99% which is good compared to existing methods.

MeSH terms

  • Breast Neoplasms / diagnostic imaging*
  • Female
  • Humans
  • Mammography / methods*
  • Models, Theoretical*
  • ROC Curve
  • Radiographic Image Enhancement*
  • Reproducibility of Results