Title: Enhanced faster R-CNN based subcutaneous and visceral adipose tissue segmentation from abdominal MRI

Authors: B. Sudha Devi; D.S. Misbha

Addresses: Department of Computer Science, Nesamony Memorial Christian College affiliated to Manonmaniam Sundaranar University, Tirunelveli, India ' Department of Computer Applications, Nesamony Memorial Christian College, India; Affiliated to: Manonmaniam Sundaranar University, Tirunelveli, India

Abstract: Obesity has emerged as a significant global problem that exposes both adults and children at risk for developing chronic diseases. The overall quantity of abdominal adipose tissue is frequently divided into two primary components, which are visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), with the former being more directly linked to health concerns. Many computer-based techniques are developed for segmenting the VAT and SAT, which are poor in feature extraction using the MRI and CT images. In this proposed model, the collected MRI and CT images of datasets 1 and 2 are enhanced using the pre-processing techniques consisting of image resizing, CLAHE, and median filter. Finally, the pre-processed images are segmented and classified using the enhanced faster R-CNN based on ResNet-5.0 and ROI (grab cut). The proposed model performance is evaluated using the performance metrics including error, accuracy, precision, specificity, etc., for datasets 1 and 2. The Enhanced Faster R-CNN model performs better by accurately segmenting and classifying the VAT and SAT from the abdominal region.

Keywords: visceral adipose tissue; VAT; subcutaneous adipose tissue; SAT; faster R-CNN; ResNet-50; grab cut; CLAHE; median filter.

DOI: 10.1504/IJCVR.2025.146297

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.3, pp.330 - 350

Received: 08 Nov 2022
Accepted: 15 Nov 2023
Published online: 19 May 2025 *

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