Recent studies on the wavelet transform and geometry of fractals indicate that microcalcification can be utilized for the study of the morphology and diagnosis of cancerous cases. In this paper we deal with the fractal modeling of the mammographic images and their background morphology. It is shown that the use of fractal modeling as applied to a given image can clearly discern cancerous zones from noncancerous areas. Our results show that fractal modeling of images can be used as an effective tool for identification of cancerous cells. For fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section. We have used two dimensional box counting algorithm after which based on the order of the computations, they are placed in an appropriate matrix to facilitate the required computations.For wavelet transform,the original image is first analysed by db2 to 3 different resolution levels and for detection of microcalcification we just need to nullify wavelet coefficients of the image at first scale and low frequency at the third scale subimages and take reverse wavelet transform of the remaining coefficients to reconstruct mammogram.Finally using eight features identified as characteristic features of microcalcification extracted from mammograms, the results obtained from the preliminary analysis stages, were utilized in a neural network for classification of cells into malignant and benign with the accuracy of 89.21 % classification results in fractal method and accuracy of 88.23 % classification results in wavelet method.
Dual tree complex wavelet transform(DTCWT) is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The purposes of de-noising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal or image. This paper proposes a method for removing white Gaussian noise from ECG signals and biomedical images. The discrete wavelet transform (DWT) is very valuable in a large scope of de-noising problems. However, it has limitations such as oscillations of the coefficients at a singularity, lack of directional selectivity in higher dimensions, aliasing and consequent shift variance. The complex wavelet transform CWT strategy that we focus on in this paper is Kingsbury's and Selesnick's dual tree CWT (DTCWT) which outperforms the critically decimated DWT in a range of applications, such as de-noising. Each complex wavelet is oriented along one of six possible directions, and the magnitude of each complex wavelet has a smooth bell-shape. In the final part of this paper, we present biomedical image and signal de-noising by the means of thresholding magnitude of the wavelet coefficients.
Proc. SPIE. 8285, International Conference on Graphic and Image Processing (ICGIP 2011)
KEYWORDS: MATLAB, Digital signal processing, Wavelet transforms, Image compression, Image processing, Wavelets, Field programmable gate arrays, Linear filtering, Discrete wavelet transforms, Transform theory
Discrete Wavelet Transform (DWT) is widely used in signal processing applications. In this paper, we describe hardware implementation of a lifting-based DWT, which is used in image compression. The CDF(2,2) lifting-based wavelet transform is modeled and simulated using MATLAB. Based on DSP methodologies, the signal flow graph and dependence graph are derived. The dependence graph is optimized and used to implement the hardware description of the circuit in Verilog. We have synthesized and implemented the circuit using both Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) design approaches. To confirm the circuit operation, post-synthesis and post-layout simulations were done for FPGA and ASIC designs, respectively.