Fractal Features based on Differential Box Counting Method for the Categorization of Digital Mammograms
نویسندگان
چکیده
Computer aided diagnostic systems can assist radiologist in detecting breast cancer at an early stage with improved mammogram interpretation efficiency. In this paper, six fractal based features obtained from the fractal dimension computed using differential box counting method, are used for distinguishing between normal mammograms from the cancerous ones. The new fractal feature f6 derived from the modified average image is found to be a better feature for distinguishing between normal, malignant and benign masses and mammograms with microcalcifications. The average values of the new normalized fractal feature for normal, mammogram with microcalcifications, benign and malignant tumors are obtained as 0.125, 0.4737, 0.2954, and 0.5992 respectively. The area under the Receiver Operating Characteristics (ROC) curve is found to be 0.923. The study is validated using the mammograms obtained from the online Mammographic Image Analysis Society (MIAS) Digital Mammogram database. KeywordsBreast Cancer, Malignant and benign masses, Microcalcifications, fractal dimension, fractal features
منابع مشابه
Classification of Mammograms into Normal, Benign and Malignant based on Fractal Features
Modern life style of women has made them more vulnerable to breast cancer and it is considered as the largest cause of mortality among women. This paper presents a novel method to classify mammograms into normal ones, with benign and malignant microcalcifications, and with malignant and benign tumors using fractal features derived from fractal dimension. Here, three fractal dimension estimation...
متن کاملAnalysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension
Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obl...
متن کاملBehavior Analysis of Fractal Features for Texture Description in Digital Images: An Experimental Study
Our Goal • Found that Fractal Features effectively have texture recognition ability using statistical, soft computing, data mining and machine learning methods. • Six texture descriptors introduced in [Chaudhuri and Sarkar, 95] are based on the Fractal Dimension of the original and derived images. The studied features were the FDs of: • the original image (F_1), • the high gray-valued image (F_...
متن کاملDiagnosis of B-CLL Leukemia Using Fractal Dimension
Background:Leukemia is cancer of blood and bone marrow cells. In general, there are four types of leukemia: chronic myelogenous leukemia (CML), acute myeloid leukemia (AML), B-cell chronic lymphocytic leukemia (CLL) and acute lymphoblastic leukemia (ALL). Fractal geometry can be introduced as one of the effective ways to detect this type of cancer. In this study, with introduc...
متن کاملIdentification of active tectonic areas using fractal analysis (box counting) of earthquakes, lines and waterways of Alborz province
Fractal analysis of earthquakes, lineages and waterways are considered as one of the practical tools to evaluate tectonic activity. With the help of fractal analysis, structural maturity and tectonic dynamics can be evaluated. In this research, using fractal analysis and box counting method and fild studies, the measured fractal dimensions for the designed network of Alborz province were studie...
متن کامل