RNN-AHF Framework: Enhancing Multi-focal Nature of Hypoxic Ischemic Encephalopathy Lesion Region in MRI Image Using Optimized Rough Neural Network Weight and Anti-Homomorphic Filter.
Authors
Affiliations (3)
Affiliations (3)
- Department of Mathematics, Sri Venkateswara College of Engineering, Affiliated to Anna University, Sriperumbudur, Chennai 602117, Tamil Nadu, India.
- Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai-601103, Tamil Nadu, India.
- Department of Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Muthapudupet, Chennai-600055, Tamil Nadu, India.
Abstract
Image enhancement of the Hypoxic-Ischemic Encephalopathy (HIE) lesion region in neonatal brain MR images is a challenging task due to the diffuse (i.e., multi-focal) nature, small size, and low contrast of the lesions. Classifying the stages of HIE is also difficult because of the unclear boundaries and edges of the lesions, which are dispersedthroughout the brain. Moreover, unclear boundaries and edges are due to chemical shifts, partial volume artifacts, and motion artifacts. Further, voxels may reflect signals from adjacent tissues. Existing algorithms perform poorly in HIE lesion enhancement due to artifacts, voxels, and the diffuse nature of the lesion. In this paper, we propose a Rough Neural Network and Anti-Homomorphic Filter (RNN-AHF) framework for the enhancement of the HIE lesion region. The RNN-AHF framework reduces the pixel dimensionality of the feature space, eliminates unnecessary pixels, and preserves essential pixels for lesion enhancement. The RNN efficiently learns and identifies pixel patterns and facilitates adaptive enhancement based on different weights in the neural network. The proposed RNN-AHF framework operates using optimized neural weights and an optimized training function. The hybridization of optimized weights and the training function enhances the lesion region with high contrast while preserving the boundaries and edges. The proposed RNN-AHF framework achieves a lesion image enhancement and classification accuracy of approximately 93.5%, which is better than traditional algorithms.