Fractal analysis is useful in digital image processing for the characterization of shape
roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale
characteristics in mammograms that vary between benign masses and malignant tumors. This book
demonstrates the use of fractal analysis to classify breast masses as benign masses or
malignant tumors based on the irregularity exhibited in their contours and the gray-scale
variability exhibited in their mammographic images. A few different approaches are described to
estimate the fractal dimension (FD) of the contour of a mass including the ruler method
box-counting method and the power spectral analysis (PSA) method. Procedures are also
described for the estimation of the FD of the gray-scale image of a mass using the blanket
method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern
classification of breast masses several other shape features and texture measures are
described in the book. The shape features described include compactness spiculation index
fractional concavity and Fourier factor. The texture measures described are statistical
measures derived from the gray-level cooccurrence matrix of the given image. Texture measures
reveal properties about the spatial distribution of the gray levels in the given image
therefore the performance of texture measures may be dependent on the resolution of the image.
For this reason an analysis of the effect of spatial resolution or pixel size on texture
measures in the classification of breast masses is presented in the book. The results
demonstrated in the book indicate that fractal analysis is more suitable for characterization
of the shape than the gray-level variations of breast masses with area under the receiver
operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses.
The methods and results presented in the book are useful for computer-aided diagnosis of breast
cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer Detection and Analysis
ofnewline Breast Masses Datasets of Images of Breast Masses Methods for Fractal Analysis
Pattern Classification Results of Classification of Breast Masses Concluding Remarks
