Alzheimer’s disease (AD) is a progressive neurodegenerative disorder dementia. The main challenges for medical investigators have been the early diagnosis of patients with AD because an early diagnosis can provide greater opportunities for patients to be eligible for more clinical trials. The transitional state between healthy control (HC) and AD with mild memory problems is Mild cognitive impairment (MCI). A reliable diagnosis of MCI can be very effective for early diagnosis of AD. In this study, a fast and accurate method based on rotation invariant descriptors is proposed and moments are used to distinguish the patients with AD and MCI from normal participants (HC) using structural Magnetic Resonance Images (MRI). The rotation invariant descriptors are among the best region based shape descriptors which are used in many medical image processing applications. The angular radial transform (ART) is one such rotation invariant descriptors. This descriptor has two essential characteristics as compared to moment based descriptors, viz., it has low computation cost and provides a large number of numerically stable features. However, its kernel consists of the sinusoidal functions which still needs high computation time. In this paper, we developed fast and effective method to compute the radial & angular sinusoidal functions using 8-way symmetry and also used fast & recursive method to extract the features from MRI images using OFMMs. These methods are used not only for binary images but for gray level images also. The proposed method is not only fast but also more reliable and numerically stable.

Alzheimer, Early Diagnosis, Rotation Invariant Descriptors, Angular Radial Transform, Mild cognitive impairment, Healthy control, Orthogonal Fourier Mellin Moments, Zernike Moments

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