Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest

Infant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-medical image segmentation based on deep learning has presented significant potential in becoming an important element of the clinical assessment process. Inspired by the mentioned objective, we introduce a methodology for analysing infant image in order to appropriately segment tissue of infant MRI images. In this paper, we integrated random forest classifier along with deep convolutional neural networks (CNN) for segmentation of infants MRI of Iseg 2017 dataset. We segmented infants MRI brain images into such as WM- white matter, GM-gray matter and CSF-cerebrospinal fluid tissues, the obtained result show that the recommended integrated CNN-RF method outperforms and archives a superior DSC-Dice similarity coefficient, MHD-Modified Hausdorff distance and ASD-Average surface distance for respective segmented tissue of infants brain MRI

A novel approach for brain tissue segmentation and classification in infants' MRI images based on seeded region growing, foster corner detection theory, and sparse autoencoder

Brain tissue segmentation and classification in infant MRI images play a crucial role in early diagnosis of neurological disorders. In this paper, we propose a novel approach based on Seeded Region Growing, Foster Corner Detection Theory, and Sparse Autoencoder. Proposed method incorporates advantages of these techniques to improve segmentation and classification accuracy of infant brain MRI. An unsupervised segmentation method proposed in this study has four stages. First stage involves pre-processing input image by removing noise using a non-local means method and rectifying intensity inhomogeneity field employing Contrast-limited adaptive histogram equalization. In second stage, modified and optimized approach of region growth is utilized, where Forstner Corner Identification principle is used to choose and position seeds to a producer. Third stage involves applying the region growth technique to image, where similarity criterion is optimized using Predator Prey algorithm. In fourth stage, tissues are classified using weighted KNN and Sparse Auto Encoder classifiers. Result shows high Dice factor of 93%, outperforming existing works. This method improves accuracy of brain tissue segmentation and classification, leading to better clinical decision-making. Applications of proposed method include early detection and diagnosis of neurological disorders, monitoring of disease progression, and assessment of treatment effectiveness