A fractal analysis approach to identification of tumor in brain MR images

Abstract

Considerable research has been pursued on fractal geometry in various aspects of image analysis and pattern recognition. Magnetic resonance (MT) images typically have a degree of randomness associated with the natural random nature of structure. Thus fractal analysis is appropriate for MR image analysis. The purpose of this study is to apply fractal analysis to identify the presence of tumor in brain MR images. For tumor detection in MR brain images, the authors propose three different fractal-based methods. For each method, the brain MR images are divided into a number of pieces. The first method involves thresholding the pixel intensity values and hence, the authors call the technique piecewise-threshold-box-counting (PTBC) method. For the subsequent methods, the intensity is treated as the third dimension. The authors implement the improved piecewise modified box-counting (PMBC) and piecewise triangular prism surface area (PTPSA) methods respectively. With the PTBC method, they find the differences in intensity histogram and fractal dimension between normal and tumor images. Using the PMBC and PTPSA methods one can detect and locate the tumor in the brain MR images more accurately. Thus, the novel techniques proposed herein offer satisfactory tumor identification.