ICTACT Journal on Image and Video Processing

M. Santhanalakshmi ¹, K. Nirmala ²

Affiliations
1 Department of Computer Applications, Manonmaniam Sundaranar University, IN
2 Department of Computer Science, Quaid-e-Millath Government College for Women, IN

Abstract

This study presents a framework for gray texture classification based on the fusion of wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT) are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performance is evaluated independently. Then feature fusion technique is applied to increase the classification accuracy of the proposed approach. Brodatz texture images are used for this study. The results show that, only two texture images D105 and D106 are misclassified by the fusion approach and 99.74% classification accuracy is obtained.

Keywords

Texture Classification, Wavelet Transform, Curvelet Transform, Nearest Neighbor Classifier, Brodatz Album.

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K. Sankar ¹, K. Nirmala ²

Affiliations
1 Department of Computer Application, Manomaniam Sundaranar University, IN
2 Department of Computer Science, Quaid-e-Millet College, IN

Abstract

Breast cancer is a common disease for women and various techniques have been used to detect the breast cancer. The mammogram images are noise, low contrast and blur due to limitations of the X-ray hardware system. So, we should enhance the mammogram images for radiologist observation. To attain this, we strongly recognize that the digital mammography is a truthful technique with a new method and also it can easily identify the breast cancer at the very early stage before any symptoms are shown. In this paper, we propose NonSubsampled Contourlet Transform (NSCT) method for enhancing the mammogram images and the comparison between 2-D HAAR Discrete Wavelet Transform and Contourlet Transform. The NSCT extracts the shift-invariant multi-scale, multi-direction and the geometric information of mammogram images which is used to distinguish noise from weak edges than existing transformations.

Keywords

Contourlet Transform, Discrete Wavelet Transform, Nonsubsampled Contourlet Transform, Mammogram Image Enhancement.

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