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Region-based processing of volumetric data

Hućko M., Śramek M.

Journal of Medical Informatics & Technologies

2011

Vol. 17

247--253

Measurement of volumetric tomographic data, similar to other measurement techniques, suffers from several classes of artifacts, of which noise presence and the partial volume effect belong to the most prominent ones. These artifacts spoil data analysis and/or visualization, which may, for example in the case of medical imaging, lead to erroneous decisions with severe consequences. We propose a set of tools for region-based processing of volumetric data. Here, the basic entity is a spectrally homogeneous region instead of the traditional voxel. This provides for, on the one hand, higher robustness and, on the other hand, speeds up processing owing to many times smaller amount of elements to work with. Homogeneous regions are in our approach detected by the well-known segmentation by means of the watershed transform. In this paper we present algorithms for streamed computation of watershed transform, which allows for processing of very large data, region-based data smoothing and region merging based on the spectral distance. Further, we present an interactive tool for volume data segmentation and visualization which takes advantage of region hierarchies obtained by a hierarchical watershed transform.

An adaptive stochastic optimization method for medical registration problem

Król Z.

Journal of Medical Informatics & Technologies

2004

Vol. 7

MIP79--88

This paper presents a methodology that addresses important issues concerned with the optimization of the misregistration measures in the volumetric medical data registration problem. Our registration framework uses robust simulated annealing method to handle multiple local minima of the cost function. Our efforts have been centred on obtaining a reliable, efficient and generally applicable method to solve such optimization problems. This has been accomplished through developing an adaptive cooling schedule for the simulated annealing method. The proposed method is very reliable for the estimation of the global minimum in the optimization of objective functions with highly differentiated search space landscapes. We present the detailed description of the method as well as discussion of its advantages and disadvantages.