An adaptive nonlinear enhancement method using sigmoid function for iris segmentation in pterygium cases

Abstract

Pterygium is an eye related disease affected by the fibrovascular tissue that encroaches into the corneal region. Recently, image processing techniques have been explored in the development of pterygium detection system. An iris segmentation module is needed to develop an automatic pterygium detection system of the anterior segment photographed images (ASPI). Qualitatively, the invasion of the pterygium tissues on the iris will result in the imperfect circular iris feature. Thus, an adaptive nonlinear enhancement method using sigmoid function have been proposed in this work to enhance the ASPI. The cutoff and gain factor of the sigmoid function are adaptively calculated based on the tested images. Fifty eight ASPI of various sizes contributed by RAFAEL have been tested using the proposed enhancement method. The proposed method proves to give better visual results, later contributes to more accurate segmented iris regions with accuracy and specificity values of 0.9353 and 0.8818, respectively.