Fractal Dimension Estimation in Diagnosing Alzheimer’s Disease

Estimated entropies from a limited data set are always biased. Consequently, it is not a trivial task to calculate the entropy in real tasks. In this paper, we used a generalized definition of entropy to evaluate the Hartley, Shannon, and Collision entropies. Moreover, we applied the Miller and Harris estimations of Shannon entropy, which are well known bias approaches based on Taylor series. Finally, these estimates were improved by Bayesian estimation of individual probabilities. These methods were tested and used for recognizing Alzheimer’s disease, using the relationship between entropy and the fractal dimension to obtain fractal dimensions of 3D brain scans

Use of fuzzy edge single-photon emission computed tomography analysis in definite Alzheimer's disease - a retrospective study

<p>Abstract</p> <p>Background</p> <p>Definite Alzheimer's disease (AD) requires neuropathological confirmation. Single-photon emission computed tomography (SPECT) may enhance diagnostic accuracy, but due to restricted sensitivity and specificity, the role of SPECT is largely limited with regard to this purpose.</p> <p>Methods</p> <p>We propose a new method of SPECT data analysis. The method is based on a combination of parietal lobe selection (as regions-of-interest (ROI)), 3D fuzzy edge detection, and 3D watershed transformation. We applied the algorithm to three-dimensional SPECT images of human brains and compared the number of watershed regions inside the ROI between AD patients and controls. The Student's two-sample t-test was used for testing domain number equity in both groups.</p> <p>Results</p> <p>AD patients had a significantly reduced number of watershed regions compared to controls (<it>p </it>< 0.01). A sensitivity of 94.1% and specificity of 80% was obtained with a threshold value of 57.11 for the watershed domain number. The narrowing of the SPECT analysis to parietal regions leads to a substantial increase in both sensitivity and specificity.</p> <p>Conclusions</p> <p>Our non-invasive, relatively low-cost, and easy method can contribute to a more precise diagnosis of AD.</p