Narrow Results

In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

The field of medical image classification has been one of the most attention-gaining research areas in the recent times due to the increasing demand for an efficient tool that can help doctors in making quick and correct diagnoses. In this paper, a hybrid feature extraction technique is proposed, which is based on discrete wavelet transform (DWT), non-subsampled contourlet transform (NSCT) and isotropic gray level co-occurrence matrix (GLCM) for the categorization of grade II, III, and IV gliomas. The proposed method was applied on a dataset of 93 MRI brain images containing three glioma grades (23 grade II, 45 grade III, and 25 grade IV). The efficiency of proposed methodology is evaluated in terms of classification accuracy, sensitivity and specificity. The highest accuracy of $88.88\%$ for grade III, sensitivity of $95.65\%$ and specificity of $95.71\%$ were achieved in case of grade II.

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.