Image Processing of Infrared Thermal Images for the Detection of Necrotizing Enterocolitis

摘要

Necrotizing Enterocolitis (NEC) is a devastating intestinal disease associated with a high rate of mortality and long-term morbidity. Treatments can be successful if NEC is diagnosed early, but no reliable methods exist. Infrared imaging can detect tissue inflammation and thus has the potential to be an early diagnostic tool for NEC. Infants with no clinical or radiographic signs of NEC, and a group of infants with evidence of at least Bell's Stage 2 NEC were enrolled in our study. Infants underwent bedside infrared imaging for 60 seconds. The dataset consists of twenty normal infants and nine infants with NEC. In early work, in infants with NEC, the upper-to-lower (UL) region temperatures differed significantly, where no significant difference in the UL region was found in normal infants. No significant difference was found in left-to-right (LR) region temperatures for both groups. The decision tree classifier produced good results in terms of specificity, sensitivity, and standard deviation for ten trials. Results for the medians were: 91%+/-0.07%; 84%+/-18%; and for the means they were: 86%+/-0.04%; 79%+/-21% [1]. In this work, we assessed the impact of image enhancement in discriminating between infants with NEC and those without. The approaches explored were: (ⅰ) noise reduction; (ⅱ) background removal; and (ⅲ) contrast enhancement. Preliminary results show marked improvement in detecting infants with NEC. Future work will automate the analysis and carry-out a prospective study to attempt detecting NEC at earlier stages. Other image analysis techniques will be tested to enhance the performance of our new diagnostic tool.