Improving image segmentation via shape PCA reconstruction

This paper proposes a post-processing method for image segmentation to take advantage of information not directly available from the image. Specifically, the proposed method improves the segmentation of an image by making use of shape information learned from training shapes in ground truth images. To obtain shape prior, training shapes are first aligned by congealing, and then landmark interpolation is performed, followed by shape PCA on aligned shapes. To improve a segmentation, subsequently, shape PCA reconstruction is performed using the first few principal components on objects in the segmented image. Shape PCA is performed locally instead of globally, on parts of the object deemed inaccurate, using a method based on radius-vector function. Experimental results show that shape PCA reconstruction, especially local shape PCA reconstruction, improves the segmentation in an ore-size measurement application significantly.