Segmentation of defects on the tree log surface remains a challenge due to the unclear seperation between the foreground and the background and the high variability of the tree surface. Even if some first works exist to process specific tree species, a generic method robust to various species is missing. We propose a new approach for segmenting defects on log surface based on the tabular object analysis. We firstly compute the log centerline by surface normal accumulation and then threshold the point cloud by the the difference between the distance to the centerline and the reference distance estimated from a patch of neighbors. The performance of the proposed approach was experimented and compared on ten logs recovered from different species. The results showed that our approach outperformed other method based on cylinder detection and was robust to several tree species. The results can be reproduced and compared on an online demonstration.