Adaptive λ-enhancement: Type I versus type II fuzzy implementation

lambda-enhancement, introduced by Tizhoosh et al., is a contrast adjustment technique that uses involutive fuzzy complements to find the best gray-level transformation in order to increase the image contrast. Applied on medical images, lambda-enhancement can provide good results with respect to visually perceived improvement of object-background discrimination. In this work, we provide two extensions of lambda-enhancement. First we extend it to employ interval-valued fuzzy sets (special case of type II fuzzy sets), and second, we provide an adaptive version of both regular (type I) and interval-value (type II) fuzzy lambda-enhancement. Using breast ultrasound images, we demonstrate the enhancement effect and compare them with the well-established CLAHE method (contrast-limited adaptive histogram equalization).