Novel Fuzzy Technique for Denoising Mammogram Images Damaged By Low and
High Impulse Noise Density.
Abstract
Abstract A Fuzzy logic based mean filter (FLBMF) is presented for
impulse noise reduction of mammogram images degraded with additive
impulse noise. FLBMF removes both low and high density impulsive noise
from mammogram images. FLBMF performs this in three major phases. In
phase one, the detection of noisy pixels is performed and determined. In
phase two, an adaptive threshold is determined by examining the
neighboring pixels. In phase three, fuzzy membership functions and fuzzy
rules are used to decide whether the current pixel is noise-free, or the
noise pixel is in a smooth or detailed region. All these phases are
based on fuzzy rules making use of membership functions. FLBMF can be
applied iteratively to effectively reduce impulsive noise. In
particular, the membership function’s shape is adapted according to the
remaining noise level after each iteration, making use of the
distribution of the homogeneity in the image. In this approach, the
mammogram images are selected from mini-MIAS database and renamed as
MammoB1, MammoB2, MammoB3 and MammoB4, are then deformed by varying
intensities of impulse noise. The performance evaluation of various
filters including FLBMF tested at low, medium and high noise densities
on different standard grey scale mammogram images is then carried out.
Mathematical performance parameters including Mean Square Error (MSE),
Peak-signal-to-noise-ratio (PSNR), and Structural Similarity Index
Measure (SSIM) are finally applied to measure the accuracy and
performance of this approach. The image modalities implementation and
analysis of our approach is carried out in MATLAB functions. Keywords:
Impulsive Noise; FLBMF, Fuzzy membership function, Fuzzy rules, Edge
preserving filtering, Fuzzy image filtering, Noise reduction