Title: Brain image enhancement and segmentation using anatomically constrained neural networks

Authors: P.S. Arthy; A. Kavitha

Addresses: Department of Electronics and Communication Engineering, Sri Sai Ram Institute of Technology, Chennai, India ' Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamilnadu, India

Abstract: Brain image segmentation is one of the processes that take the most time and is the most complicated to do in a therapeutic scenario. The essential principles and features of medical image segmentation based on deep learning are presented. MRI-based medical image classification issues are addressed in this study using a histogram and time-frequency differential deep (HTF-DD) technique. The following are the stages of the proposed approach's construction. An unsupervised training procedure is used to build a deep convolutional neural network (CNN), which then outputs standardised improved pre-processed features for data extraction. Secondly, a set of time-frequency characteristics is derived from medical images using the time signal and the frequency signal. The last step is to develop an effective model based on differential deep learning for classifying objects. Multi-modal brain data sets and public standards are used to illustrate the applicability of our methodology.

Keywords: neural network; image processing; brain image enhancement; segmentation; anatomically constrained neural networks; ACNNs; histogram and time-frequency differential deep; HTF-DD; convolutional neural network; CNN.

DOI: 10.1504/IJMEI.2024.141795

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.6, pp.537 - 546

Received: 19 Mar 2022
Accepted: 15 Jun 2022
Published online: 02 Oct 2024 *

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