K. Vaidehi ¹, T. S. Subashini ¹

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, 608002, IN

Abstract

Worldwide, breast cancer is one of the top two lethal diseases among women. Breast tissue density is the important risk indicator of breast cancer. Digital Mammography technique is used to detect the breast cancer at its benign stage. Computer Aided Diagnosis (CAD) tools aids the radiologist for an accurate diagnosis and interpretation. In this work, Statistical features are extracted from the Region of Interest (ROI) of the breast parenchymal region. K-NN with three different distance metrics namely Euclidean, Cosine, City-block and its combination is used for classification. The extracted features are fed into the classifier to classify the ROI into any of three breast tissue classes such as dense, fatty, glandular. The classification accuracy obtained for combined k-NN is 91.16%.

Keywords

Breast Density, K-NN, Mammography, Statistical Descriptors.

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D. Vaishnavi ¹, T. S. Subashini ¹

Affiliations
1 Department of Computer science and Engineering, Annamalai University, Tamilnadu, IN

Abstract

Digital images are widely used and can be easily altered through the internet medium. Therefore, this article proposes a novel method of fragile watermarking to detect the image tampers. The proposed method is implemented by an edge image and chaotic Arnold map. The idea of edge detection is to make the image as a binary version and also significantly decrease the sum of information in an image and conserving the structural properties. The edge image is obtained from the watermark image using the Canny edge detection operator. The security and sensitivity of the proposed method is enhanced by employing the Arnold map before embedding the edge image. The accuracy of tamper detection depends upon the variations made in the image and it highlights a location where the tamper has been done. The quality of the watermarked image is evaluated using the metrics Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). The experiments were carried out to assess the performance of the proposed method for several forms of fiddling with different images. The result shows that the proposed method efficiently localizes the tampered regions.

Keywords

Fragile Watermarking, Tamper Detection, Chaotic Map, Edge Image, PSNR, NC.

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G. N. Balaji ¹, T. S. Subashini ¹, N. Chidambaram ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar- 608002, IN
2 Department of Cardiology, Annamalai University, Annamalai Nagar-608002, IN

Abstract

Objectives: Automating cardiac view classification is the first step for automating computer aided cardiac disease diagnosis. In this paper automatic cardiac view classification system is proposed. Methods: This system attempts to classify four standard cardiac views in echocardiogram namely Parasternal Long Axis (PLAX), Parasternal Short Axis (PSAX), Apical Four Chamber (A4C), and Apical Two Chamber (A2C) views automatically using Speed Up Robust Features (SURF). Conclusion: The Speed Up Robust Features is effective in collecting more class-specific information and ro-bust in dealing with partial occlusion and viewpoint changes. To authenticate the generalizability and robustness, the proposed system is tested on a dataset of 200 echocardiogram images which achieve a classification rate of 90.7%.

Keywords

Apical Four Chamber (A4C), Apical Two Chamber (A2C), Echocardiogram, Parasternal Long Axis (PLAX), Parasternal Short Axis (PSAX), Speed Up Robust Features (SURF).

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