Bioinspired Applications in Artificial and Natural by Jose Mira, José M. Ferrández, Jose-Ramon Alvarez Sanchez,

By Jose Mira, José M. Ferrández, Jose-Ramon Alvarez Sanchez, Felix Paz, Javier Toledo

Mira J., Ferrandez J.M., Jose-Ramon Alvarez Sanchez, de Paz F.l., Toledo F.J. (eds.) Bioinspired functions in man made and average Computation (Springer, 2009)(ISBN 3642022669)(O)(550s)

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Genetic approaches for topological active nets optimization. Pattern Recognition 42, 907–917 (2009) 7. : Automated 3D segmentation using deformable models and fuzzy affinity. , Gindi, G. ) IPMI 1997. LNCS, vol. 1230, pp. 113–126. Springer, Heidelberg (1997) 8. : Comparison of 3D deformable models for in vivo measurements of mouse embryo from 3D ultrasound images. In: Ultrasonics Symposium, 2004, vol. 1, pp. 748–751. IEEE, Los Alamitos (2004) 9. : Topology adaptive deformable surfaces for medical image volume segmentation.

Figure 3 shows an example of this set. 3100 dB for original vs artefacted images, what implies an increase of 3dB. a) b) c) Fig. 3. a) Original image b) Synthetically artefacted image c) Processed image Since it has been commented previously, most of the commercial applications threshold the image to define the structure to reconstruct. Figure 4 shows the algorithm input images and the result of thresholding them (with a threshold value of 130), and the same for the processed ones. The original dental images are remarkably affected by strong artifacts such as streaking and beam hardening and noise.

The image is composed of a sequence of CT images that contain two bones, a tibia and a fibula. Figure 6(a) represents a slice of this CT image set. Figure 6(b) shows the 3D reconstruction from the 2D slices. In addition of the fuzziness of the contours of the two bones, the external contour of the leg introduces a contrast in the background gray level that the algorithms must overcome. Due to this, the greedy approach cannot achieve a good segmentation (Figure 6(c)) meanwhile the hybrid algorithm overcomes the external contour and the image noise to provide a correct division of the subnets (Figure 6(d)).

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