9 research outputs found
Medical image registration using unsupervised deep neural network: A scoping literature review
In medicine, image registration is vital in image-guided interventions and
other clinical applications. However, it is a difficult subject to be addressed
which by the advent of machine learning, there have been considerable progress
in algorithmic performance has recently been achieved for medical image
registration in this area. The implementation of deep neural networks provides
an opportunity for some medical applications such as conducting image
registration in less time with high accuracy, playing a key role in countering
tumors during the operation. The current study presents a comprehensive scoping
review on the state-of-the-art literature of medical image registration studies
based on unsupervised deep neural networks is conducted, encompassing all the
related studies published in this field to this date. Here, we have tried to
summarize the latest developments and applications of unsupervised deep
learning-based registration methods in the medical field. Fundamental and main
concepts, techniques, statistical analysis from different viewpoints,
novelties, and future directions are elaborately discussed and conveyed in the
current comprehensive scoping review. Besides, this review hopes to help those
active readers, who are riveted by this field, achieve deep insight into this
exciting field
On Pattern Classification Using Statistical Moments
Selecting appropriate feature extraction method is absolutely one of the most important factors to archive high classification performance in pattern recognition systems. Among different feature extraction methods proposed for pattern recognition, statistical moments seem to be so promising. Whereas theoretical comparison of the moments is too complicated, in this paper, an experimental evaluation on four well known statistical moments namely Hu invariant moments, Affine invariant moments, Zernike moments, and Pseudo-Zernike moments is presented. Set of different experiments on a binary images dataset consisting of regular, translated, rotated, and scaled Persian printed numerical characters using a nearest neighbor rule classifier has been done and variety of interesting results have been presented. Finally, the results show that Pseudo-Zernike moments outperform the other introduced moments