Computational methodology for automatic detection of strabismus in digital images through Hirschberg test
Strabismus is a pathology that affects about 4% of the population, causing aesthetic problems, reversible at any age; however, problems that can also cause irreversible muscular alterations, and alter the vision mechanism. The Hirschberg test is one of the exams used to detect this pathology. The application of high technology resources to help diagnose and treat ophthalmological conditions is, lamentably, not commonly found in the sub-specialty of strabismus. This work presents a methodology for automatic detection of strabismus in digital images through the Hirschberg test. For such, the work was organized into four stages: (1) finding the region of the eyes; (2) determining the precise location of the eyes; (3) locating the limbus and brightness; and (4) identifying strabismus. The methodology has produced results on the range of 100% sensibility, 91.3% specificity and 94% for the correct identification of strabismus, ensuring the efficiency of its geostatistical functions for the extraction of eye texture and for the calculation of the alignment between the eyes on digital images obtained from the Hirschberg test.
strabismus hirschberg test geostatistical functions medical image image processing pattern recognition support vector machine