Symmetry is an important feature of human anatomy and the absence of symmetry in medical images can indicate the presence of pathology. Quantification of image symmetry can then be used to improve the automatic analysis of medical images.Methods
A method is presented that computes both local and global symmetry in 2D medical images. A symmetry axis is determined to define for each position p in the image a mirrored position Symbol on the contralateral side of the axis. In the neighborhood of Symbol, an optimally corresponding position Symbol is determined by minimizing a cost function d that combines intensity differences in a patch around p and the mirrored patch around Symbol and the spatial distance between Symbol and Symbol. The optimal value of d is used as a measure of local symmetry s. The average of all values of s, indicated as S, quantifies global symmetry. Starting from an initial approximation of the symmetry axis, the optimal orientation and position of the axis is determined by greedy minimization of S.Results
The method was evaluated in three experiments concerning abnormality detection in frontal chest radiographs. In the first experiment, global symmetry S was used to discriminate between 174 normal images and 174 images containing diffuse textural abnormalities from the publicly available CRASS database of tuberculosis suspects. Performance, measured as area under the receiver operating characteristic curve Symbol was 0.838. The second experiment investigated whether adding the local symmetry s as an additional feature to a set of 106 texture features resulted in improvements in classifying local patches in the same image database. We found that Symbol increased from 0.878 to 0.891 (P = 0.001). In the third experiment, it was shown that the contrast of pulmonary nodules, obtained from the publicly available JSRT database, increased significantly in the local symmetry map compared to the original image.Conclusions
We conclude that the proposed algorithm for symmetry computation provides informative features which can be used to improve abnormality detection in medical images both at a local and a global level.