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Recent Patents on Computer Science


ISSN (Print): 2213-2759
ISSN (Online): 1874-4796

Research Article

A Multiresolution Ripplet Transform for Breast Cancer Diagnosis in Digital Mammograms

Author(s): Jeevanayagam Anitha and James D. Peter

Volume 9, Issue 3, 2016

Page: [195 - 202] Pages: 8

DOI: 10.2174/2213275908666150324223944

Price: $65


Background: Breast cancer is diagnosed as the most leading and dangerous cancer in women all over the world. Recent patents have shown that breast cancer is the second leading cause of death worldwide, among other cancers. This paper presents a development of breast cancer diagnosis system in digital mammograms using multiresolution technique.

Methods: The proposed method uses the Ripplet Transform (RT) which holds potential properties for feature extraction. The input mammogram image is first decomposed with the ripplet transform at different scales. Then the statistical features are extracted from each scale that is used as a feature vector. The Support Vector Machine (SVM) classifier is used as a classifier to distinguish between normal and abnormal and to classify the abnormality between benign and malignant images.

Results: In this paper, the application of multiresolution based ripplet transform in feature extraction for mammogram classification has been demonstrated. A comparative analysis is performed with the features extracted from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Curvelet Transform (CT). The experimental results demonstrate that the ripplet transform based feature extraction is an efficient and promising tool for the successful classification of digital mammograms. The average classification rate achieved for normal and abnormal is 94.41% with curvelet features, 92.68% with wavelet features, 91.75% with GLCM features. RT exploits the multiscale property along with high degree of directionality so it achieves relatively higher average classification rate of 95.08%. The average classification rate obtained abnormalities between benign and malignant through ripplet transform coefficients is 95.56%. Also the average classification rate obtained is 94.17% for curvelet, 93.61% for wavelet and 90.28% for GLCM.

Conclusion: In this paper, the advantages of ripplet transform in mammogram analysis are exploited and a new model is proposed using a ripplet transform for feature extraction and classification of mammogram images. The statistical features extracted from the ripplet transform coefficients are employed in the SVM to classify the ROI into normal and abnormal and to differentiate abnormality between benign and malignant. When compared to other multiresolution transforms, ripplet offers improvement by representing the images with singularities along smooth curves. The experimental results show that the proposed method using the ripplet transform coefficients achieves relatively improved classification rate than the other multiresolution feature extraction methods.

Keywords: Ripplet transform, mammogram diagnosis, multiresolution analysis, feature extraction, support vector machine.

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