Image segmentation plays a crucial role in many medical imaging applications and is an important but inherently difficult problem. The paper discuses the method that classify unsupervised image using a Kohonen self-organizing map neural network. This method exits two problems: training time of the network is too long and the classified result and quantity were bigger influenced by the noise of image. Two-dimensional Discrete Wavelet Transforms (DWT) decompose MRI image into the small size and denoise approximation images. Kohonen self-organizing map neural network is trained with approximation image, then trained neural network classify pixels of original image. Training time of the network was notability decrease and the classified quality influenced by the noise of image was notability reduce. The technique presented here has shown a very encouraging level of performance for the problem of segmentation in MRI image of the head.
We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.
Multiwavelets have orthogonality, compacted support and symmetry simultaneously, these properties are very important for signal processing. However, most of Multiwavelets require related prefilters. An approach to construction of symmetry/antisymmetry orthogonal filter is proposed and its corresponding balanced filter is constructed, no any prefilter is necessary. Experimental results prove its performance is superior to DGHM and CL multiwavelets, higher than Bi9/7.
Digital audio watermarking embeds inaudible information into digital audio data for the purposes of copyright protection, ownership verification, covert communication, and/or auxiliary data carrying. In this paper, we present a novel watermarking scheme to embed a meaningful gray image into digital audio by quantizing the wavelet coefficients (using integer lifting wavelet transform) of audio samples. Our audio-dependent watermarking procedure directly exploits temporal and frequency perceptual masking of the human auditory system (HAS) to guarantee that the embedded watermark image is inaudible and robust. The watermark is constructed by utilizing still image compression technique, breaking each audio clip into smaller segments, selecting the perceptually significant audio segments to wavelet transform, and quantizing the perceptually significant wavelet coefficients. The proposed watermarking algorithm can extract the watermark image without the help from the original digital audio signals. We also demonstrate the robustness of that watermarking procedure to audio degradations and distortions, e.g., those that result from noise adding, MPEG compression, low pass filtering, resampling, and requantization.