Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
Proc. SPIE. 8669, Medical Imaging 2013: Image Processing
KEYWORDS: Image segmentation, Principal component analysis, Data modeling, Medical imaging, Image processing algorithms and systems, Spectral models, Algorithm development, Dysprosium, Control systems, Fuzzy logic
We present a novel method to incorporate prior knowledge into normalized cuts. The prior is incorporated into
the cost function by maximizing the similarity of the prior to one partition and the dissimilarity to the other. This
simple formulation can also be extended to multiple priors to allow the modeling of the shape variations. A shape
model obtained by PCA on a training set can be easily integrated into the new framework. This is in contrast
to other methods which usually incorporate the prior knowledge by hard constraints during optimization. The
eigenvalue problem inferred by spectral relaxation is not sparse, but can still be solved efficiently. We apply this
method to toy and real data and compare it with other normalized cut based segmentation algorithms and graph
cuts. We demonstrate that our method gives promising results and can still give a good segmentation even when
the prior is not accurate.