Automatic Analysis of Ultrasound Images to Estimate Subcutaneous and Visceral Fat and Muscle Tissue in Patients with Suspected Malnutrition.

Malnutrition is a prevalent condition associated with adverse health outcomes, requiring the accurate assessment of muscle composition and fat distribution. This study presents a novel method for the automatic analysis of ultrasound images to estimate subcutaneous and visceral fat, as well as muscle, in patients with suspected malnutrition. The proposed system utilizes computer vision techniques to segment regions of interest (ROIs), calculate relevant variables, and store data for clinical evaluation. Unlike traditional segmentation methods that rely solely on thresholding or pre-defined masks, our method employs an iterative hierarchical approach to refine contour detection and improve localization accuracy. A dataset of abdominal and leg ultrasound images, captured in both longitudinal and transversal planes, was analyzed. Results showed higher precision for longitudinal scans compared to transversal scans, particularly for length-related variables, with the Y-axis Vastus intermediate achieving a precision of 92.87%. However, area-based measurements demonstrated lower precision due to differences between manual adjustments by experts and automatic geometric approximations. These findings highlight the system's potential for clinical use while emphasizing the need for further algorithmic refinements to improve precision in area calculations.