Analysis of bone images to recognize animals using geometric shape properties

An approach is presented for animal bone image analysis using geometric shape properties. The proposed approach, which is based on image processing techniques, has applications in medicine and veterinary anatomy studies, orthopedics, paleontology, and archaeology. Several image features, including geometric and moment invariants (regular and Zernike), are derived for recognition. The first-level classijcation is used to distinguish dzfferent kinds of bone and the second-level to recognize the right animal the bone is belong to. Two-dimensional structures, namely cluster-property and cluster-features matrices, have been employed to evaIuate dzferent bone's characteristics. Experimental results at the first- evel recognition exhibit better performance of the geometric features compared to moment invariants and Zernike moments. On the other hand, Zernike moments showed supremacy in differential diagnosis at the second level to recognize animals.

Keywords:

Image analysis , Pattern recognition , Segmentation , Moment invariants , Geometric features