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Journal of Medical Systems

, 43:40 | Cite as

A Tri- State Filter for the Removal of Salt and Pepper Noise in Mammogram Images

  • Varatharajan RamachandranEmail author
  • Vasanth Kishorebabu
Image & Signal Processing
  • 19 Downloads
Part of the following topical collections:
  1. Advancements in Internet of Medical Things for Healthcare System

Abstract

A new algorithm which uses tree based decision and tri-state non linear values to eliminate high density outlier noise in mammogram images is proposed. The proposed algorithm uses the number of non noisy pixels in the current processing vicinity as values in the decision tree. Tri-state values such as unsymmetrical truncated median or modified Winsorized mean or midpoint replaces the corrupted pixel based on the decision tree in the current processing kernel. The algorithm exhibits good PSNR, IEF, low MSE and High structural preservation property even after removing high density noise. The performance of the proposed algorithm was also found good visually. The Key aspect of the work is the combination of tree based decision and tri-state non linear values which preserves the information content of images that are required for further processing.

Keywords

Detail preserving algorithm Outlier noise Mammogram images Non linear filter 

Abbreviations

DBA

Decision based algorithm

SPN

Salt and pepper noise removal

PSNR

Peak signal to noise ratio

MSE

Mean square error

IEF

Image enhancement factor

SSIM

Structural similarity index metric

SMF

Standard median filter

AMF

Adaptive median filter

PSMF

Progressive switched median filter

IDBA

Improved decision based median filter

CUTMF

Cascaded unsymmetrical trimmed median filter

CUTMPF

Cascaded unsymmetrical trimmed midpoint filter

MDBUTMF

Modified decision based unsymmetrical trimmed median filter

DBUTMPF

Decision based unsymmetrical trimmed midpoint filter

DBSIF

Decision based spline interpolation filter

TSF

Trisate filter (proposed algorithm)

List of symbols

X

Probability density function

Sa,b

Corresponding pixel of the noisy image

K(a,b)

Current processing pixel

S1(a,b)

Array holding sorted pixels of current processing window

MXN

Size of the image

x

Original image

y

Restored image

n

Corrupted image

count

Number of non noisy pixels in the current processing window

μx

Average of the original image

μy

Average of the restored image

L

Dynamic range of pixel value

σx

Standard deviation of the original image

σy

Standard deviation of the restored image

i

Kernel size of original image

j

Kernal size of the restored image

Notes

Compliance with ethical standards

Conflict of Interest

Varatharajan declares that he has no conflict of interest. Vasanth Kishorebabu declares that he/she has no conflict of interest.

Ethical Approval

Articles do not contain studies with human participants or animals by any of the authors. Images used in the work are stored in database given in website given in reference 30.The images used in the database can be used for research.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Varatharajan Ramachandran
    • 1
    Email author
  • Vasanth Kishorebabu
    • 2
  1. 1.Sri Ramanujar Engineering CollegeKancheepuramIndia
  2. 2.Vidya Jyothi Institute of TechnologyKancheepuramIndia

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