Neonatal necrotizing enterocolitis (NEC) is one of the most severe digestive tract emergencies in neonates, involving bowel edema, hemorrhage, and necrosis, and can lead to serious complications including death. Since it is difficult to diagnose early, the morbidity and mortality rates are high due to severe complications in later stages of NEC and thus early detection is key to the treatment of NEC. In this thesis, a novel automatic image acquisition and analysis system combining a color and depth (RGB-D) sensor with an infrared (IR) camera is proposed for NEC diagnosis. A design for sensors configuration and a data acquisition process are introduced. A calibration method between the three cameras is described which aims to ensure frames synchronization and observation consistency among the color, depth, and IR images. Subsequently, complete segmentation procedures based on the original color, depth, and IR information are proposed to automatically separate the human body from the background, remove other interfering items, identify feature points on the human body joints, distinguish the human torso and limbs, and extract the abdominal region of interest. Finally, first-order statistical analysis is performed on thermal data collected over the entire extracted abdominal region to compare differences in thermal data distribution between different patient groups. Experimental validation in a real clinical environment is reported and shows encouraging results.