Application of wavelets and fractal-based methods for detection of Microcalcification in Mammograms, A Comparative Analysis using Neural Network

摘要

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.