MATLAB Code for Red Eye Retina Detection

Red eye detection is a significant topic in both medical image processing and photographic post-processing. For detecting red eye regions in retinal images, the process typically involves several key steps. First, image preprocessing is essential. Original retinal images may contain noise or uneven illumination, requiring filtering techniques such as Gaussian filtering to smooth the image while enhancing contrast to better highlight red eye regions. In MATLAB implementation, this can be achieved using imgaussfilt() for Gaussian filtering and imadjust() or histeq() for contrast enhancement. Next, color space conversion is crucial. RGB images need to be converted to color spaces more suitable for color analysis, such as HSV or Lab. Red color exhibits distinct hue characteristics in HSV space, allowing red regions to be identified by setting appropriate threshold ranges. The MATLAB code would use rgb2hsv() for conversion and create logical masks based on hue threshold values (typically 0.96-1.0 or 0.0-0.04 for red tones). Then, morphological operations help optimize detection results. Opening operations (erosion followed by dilation) can remove small noise points, while closing operations (dilation followed by erosion) can fill holes within red regions, making the detected red eye structures more complete. MATLAB's imopen() and imclose() functions implement these operations using structuring elements like strel('disk', radius). Finally, region screening and contour analysis ensure detection accuracy. By calculating features of connected regions such as area, circularity, and eccentricity, false detection regions that don't conform to red eye morphology can be eliminated. Genuine red eye regions typically appear circular and exhibit high red saturation. The MATLAB implementation would use regionprops() function to extract region properties and apply logical filtering based on these characteristics. In medical applications, red eye detection can be used to analyze retinal vascular abnormalities, hemorrhages, or other pathologies, making algorithm robustness particularly important. Appropriate parameter tuning and integration with machine learning methods can further improve detection performance, potentially using MATLAB's Classification Learner app or custom SVM implementations for advanced classification.