Computer Aided Diagnosis of Abdomen Diseases Using Curvelet Transform

Abdomen related diseases are responsible of many deaths every year. These deaths can be reduced by early diagnosis of abdomen diseases. Computer aided diagnosis (CAD) can play vital role in early detection of diseases. Hence, a novel CAD is proposed in this paper that can diagnose abdomen diseases like Hepatocellular carcinoma, cysts and Calculi using statistical curvelet texture descriptors. The proposed CAD is divided into four stages: (a) Image segmentation using active contours, (b) feature extraction, (c) feature selection and (d) abdomen disease classification. The regions of interest (ROIs) are segmented from 120[Formula: see text]CT images using active contour models. The statistical features are extracted from segmented ROIs. Further, the classifiers are used to evaluate the ability of feature set in diagnosis various diseases of abdomen. The performance metrics indicates that the proposed CAD achieves accuracy of 87.9% using curvelet coefficient features and neural network as classifier.

A ROBUST WATERMARKING SCHEME USING SELECTIVE CURVELET COEFFICIENTS

In this paper, a selective curvelet coefficient digital watermarking algorithm is proposed. Traditionally, curvelet watermarks are embedded into all sample frequency bands. However, the study of individual band behavior and the use of single band for watermarking have not been reported. The selective band will provide an addition security feature against any physical tampering. This paper aims to give an intensive study on the robustness of watermarking using selective curvelet coefficients from a single band and to find out the best band for embedding watermark. Wrapping of specially selected Fourier samples is employed to implement Fast Discrete Curvelet Transforms (FDCT) to transform the digital image to the curvelet domain.