Accurate pupil extraction algorithm by using integrated method

As vertigo disease is diagnosed by observing involuntary eye movement, position of pupil is an important parameter for nystagmus analysis system. Accurate and precise pupil extraction is necessary. In this paper, we improve accuracy of pupil extraction algorithm by using integrated method. It consists of three processes: primary pupil extraction, noise elimination, and shape estimation. Image sequence is used as input of system. Pupil is captured by infrared camera mounted on binocular. For first step, primary pupil in a frame is extracted. An adaptive threshold is applied to extraction pupil preliminary. Black blob is defined as primary pupil. However, noise is occurred in the result. To eliminate the noise, Mahalanobis distance techniques is used. In some cases, pupil is occluded by eyelash or eyelid, complete shape of pupil is estimated by ellipse. Performance of proposed method is evaluated by accuracy. There are 1869 frames of test data. Accuracy and precision are 94.06% and 1.92 pixels of error, respectively. Advantage of our method over other existing research is that criteria threshold is adaptive according to individual illumination condition of each frame, and the accuracy is improved from our previous work [18, 19, 20] by using black blob in noise elimination process.