Pattern Recognition Image Color for Premature Rupture of Membranes Diagnosis Using Euclidean Algorithm

Premature rupture of membranes must be detected accurately. Inaccurate diagnosis increases the risk of caesarean section and infection for both mother and fetus. At present, diagnosis for premature rupture of membranes is performed using litmus paper. Measurement is made based on changes of color on this paper. Unfortunately, the method is subjective so the diagnosis is not accurate. It is a subjective visualization method by midwives. This research proposes a system of digital detection for premature rupture of membranes using digital image processing developed with Euclidean algorithm. The algorithm has been widely used in medical services, one of which is to help diagnose related to images. Euclidean Algorithm shows much better performance on all of the two- and three-dimensional images with variant image contents. Data processing is performed by characterizing alteration in litmus paper color into the color elements of red, green, and blue. Measurement is accurate if the device can clearly differentiate concentrations of urine, vaginal discharge, and amniotic fluid based on color resemblance pattern. Results show that accuracy of litmus paper for detection of premature rupture of membranes using Euclidean algorithm is 95%.