Moles are used for identifying a person and can be located on any part of the human body. Mole growth may cause melanoma and cancer. Early detection and growth can help to avoid cancer and loss of visibility. The proposed algorithm is used for detecting moles in the human eye sclera. An eye mole image is the input image for the proposed algorithm. This input image is preprocessed using gray-scale conversion and a median filter. The filtered image undergoes binary conversion and morphological operations. Functions such as morphological dilation, strel, dilation, area close, binary complement, and border clear are applied to retain the mole area in the eye image. An object area detection (OAD) algorithm is applied to search the regions of the mole boundary. It identifies the mole boundary and computes the regions for mole segmentation. The segmented moles are validated within an average execution time of 0.903s.