Effect of presence/absence of noise in mammogram images using fuzzy soft set based classification

Effective use of feature set and selection of a suitable classification method are significant for improving classification accuracy. However, mammogram images classification is affected by many factors such as additive gaussian noise, low contrast and artifacts. Therefore, the aim of this paper is to observe the impact of presence /absence of noise on the quality and classification accuracy of mammogram images. The proposed methodology involved five steps that are data collection, images de-noising using wavelet hard and soft thresholding, region of interest (ROI) identification, feature extraction (statistical texture features), and classification. Hundred and twelve images (68 benign images and 51 malignant images) were used for experimental set ups. The experimental results show the improvement of classification accuracy in the presence of noise using wavelet filter with fuzzy soft set classifier compared with the results in the absence of noise within existing classification algorithms.

[1]  Ishpreet Singh Virk,et al.  Medical Image Denoising In The Wavelet Domain Using Haar And DB3 Filtering , 2012 .

[2]  N. Suthanthira Vanitha,et al.  The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images , 2013 .

[3]  Rangaraj M. Rangayyan,et al.  A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs , 2007, J. Frankl. Inst..

[4]  Nalini Singh,et al.  Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering , 2011 .

[5]  Mustafa Mat Deris,et al.  Similarity Approach on Fuzzy Soft Set Based Numerical Data Classification , 2011, ICSECS.

[6]  Norhalina Senan,et al.  De-noising analysis of mammogram images in the wavelet domain using hard and soft thresholding , 2014, 2014 4th World Congress on Information and Communication Technologies (WICT 2014).

[7]  Tae-Sun Choi,et al.  Quantum and impulse noise filtering from breast mammogram images , 2012, Comput. Methods Programs Biomed..

[8]  Ying Zheng,et al.  Analysis of signal de-noising method based on an improved wavelet thresholding , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[9]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[10]  S Kavitha,et al.  Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine , 2011, 2011 Third International Conference on Advanced Computing.

[11]  A. R. Roy,et al.  A fuzzy soft set theoretic approach to decision making problems , 2007 .

[12]  Ajoy Kumar Ray,et al.  Texture Classification Using a Novel, Soft-Set Theory Based Classification Algorithm , 2006, ACCV.

[13]  Fahd Alharbi.  Novel High Quality Data Hiding System , 2013, International Journal of Image, Graphics and Signal Processing.

[14]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[15]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[16]  Saroj Kumar Lenka,et al.  A REVIEW ON COMPUTER AIDED MAMMOGRAPHY FOR BREA ST CANCER DIAGNOSIS AND CLASSIFICATION USING IMAGE MINING METHODOL OG Y , 2011 .