Semiautomatic contour tracking method for biological object segmentation in thin-section electron microscope images with modified zero DC component–type Gabor wavelets

Abstract

Abstract In electron microscopic image processing, artificial intelligence (AI) is a powerful method for segmentation. Because creating training data remains time-consuming and burdensome, a simple and accurate segmentation tool, which is effective and does not rely on manual drawings, is necessary to create training data for AI and to support immediate image analysis. A Gabor wavelet–based contour tracking method has been devised as a step toward realizing such a tool. Although many papers on Gabor filter–based and Gabor filter bank–based texture segmentations have been published, previous studies did not apply the Gabor wavelet–based method to straightforwardly detect membrane-like ridges and step edges for segmentation because earlier works used a nonzero DC component–type Gabor wavelets. The DC component has a serious flaw in such detection. Although the DC component can be removed by a formula that satisfies the wavelet theory or by a log-Gabor function, this is not practical for the proposed scheme. Herein, we devised modified zero DC component–type Gabor wavelets. The proposed method can practically confine a wavelet within a small image area. This type of Gabor wavelet can appropriately track various contours of organelles appearing in thin-section transmission electron microscope images prepared by the freeze-substitution fixation method. The proposed method not only more accurately tracks ridge and step edge contours but also tracks pattern boundary contours consisting of slightly different image patterns. Simulations verified these results.