@article {JOSEPH2021111301,
	title = {Multifractal theory based breast tissue characterization for early detection of breast cancer},
	journal = {Chaos, Solitons \& Fractals},
	volume = {152},
	year = {2021},
	note = {n/a},
	pages = {111301},
	abstract = {<p>Mammography has proven to be the most effective tool for detecting breast cancer in its earliest and treatable stage. This paper investigates various phases of mammogram image analysis and different abnormality detection techniques in the mammogram. The present study{\textquoteright}s primary aim is to apply the Multifractal theory for the analysis of digital mammograms. The mammogram images are x-raying images of the breast and grayscale in nature. The grayscale mammogram images are processed using image processing techniques and then analyzed by multifractal characteristics. Initially, simple thresholding used to avoid artefacts and noises in the mammograms taken from the MIAS dataset of 322 images. The thresholded images resulted in sharp edges and hence smoothened using a Gaussian filter of appropriate configuration. A novel feature extraction method is proposed based on multifractal spectral parameters. The multifractal characteristics of each mammogram plotted, and multifractal features extracted from the spectrum. The major multifractal features are the bandwidth of the spectrum, the height of the spectrum, maximum and minimum singularity exponent, peak singularity exponent, and the strength of multifractality. Out of the chosen multifractal features, the width of the spectrum, the height of the spectrum, minimum value for the strength of multifractality found to be the most significant features after conducting statistical analysis using ANOVA (Analysis of Variance). We propose a novel lesion localization method based on the extracted multifractal spectral parameters. The pectoral muscle present in the mammogram removed, and the mammogram image divided into four quadrants. The lesion or mass location in a specific quadrant is identified based on the variation in the quadrant{\textquoteright}s multifractal characteristics. This method will reduce the search space of lesion in a single mammogram. Thus, the present study revealed that mammogram images exhibit multifractal behaviour. The multifractal parameters appear to be valuable biomarkers for quantitative assessment of breast tissue in the mammogram, which helps to diagnose breast cancer in its early stage.</p>
},
	keywords = {Breast Cancer, Classification, Computer Aided Detection, Mass Localization, Multifractal Analysis},
	issn = {0960-0779},
	doi = {https://doi.org/10.1016/j.chaos.2021.111301},
	url = {https://www.sciencedirect.com/science/article/pii/S096007792100655X},
	author = {Annie Julie Joseph and Pournami, P. N.}
}
