2 research outputs found
Faster estimation of the correlation fractal dimension using box-counting
Fractal dimension is widely adopted in spatial databases and data mining,
among others as a measure of dataset skewness. State-of-the-art algorithms for
estimating the fractal dimension exhibit linear runtime complexity whether
based on box-counting or approximation schemes. In this paper, we revisit a
correlation fractal dimension estimation algorithm that redundantly rescans the
dataset and, extending that work, we propose another linear, yet faster and as
accurate method, which completes in a single pass.Comment: 4 pages, to appear in BCI 2009 - 4th Balkan Conference in Informatic
Quantification of Trabeculae Inside the Heart from MRI Using Fractal Analysis
Left ventricular non-compaction (LVNC) is a rare cardiomyopathy (CMP) that
should be considered as a possible diagnosis because of its potential
complications which are heart failure, ventricular arrhythmias, and embolic
events. For analysis cardiac functionality, extracting information from the
Left ventricular (LV) is already a broad field of Medical Imaging. Different
algorithms and strategies ranging that is semiautomated or automated has
already been developed to get useful information from such a critical structure
of heart. Trabeculae in the heart undergoes difference changes like solid from
spongy. Due to failure of this process left ventricle non-compaction occurred.
In this project, we will demonstrate the fractal dimension (FD) and manual
segmentation of the Magnetic Resonance Imaging (MRI) of the heart that quantify
amount of trabeculae inside the heart. The greater the value of fractal
dimension inside the heart indicates the greater complex pattern of the
trabeculae in the heart