By Tavares J., Jorge R. (eds.)
The current e-book includes prolonged models of papers offered within the foreign convention VIPIMAGE 2007 – ECCOMAS Thematic convention on Computational imaginative and prescient and clinical picture, held in Faculdade de Engenharia da Universidade do Porto, in 17-19 of October 2007. This convention used to be the 1st ECCOMAS thematic convention on computational imaginative and prescient and clinical snapshot processing. It covers subject matters similar with picture processing and research, clinical imaging and computational modeling and simulation, contemplating their multidisciplinary. This booklet collects the cutting-edge study, equipment and new rules with regards to computational imaginative and prescient and scientific photograph processing contributing for the advance of those parts of data.
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Extra resources for Advances in computational vision and medical image processing
Ideal spatial adaptation via wavelet shrinkage. Biometrika 81(1994) 425–455 49. : Wavelet transform domain filters: A spatially selective noise filtration technique. IEEE Transactions on Image Processing 3(6) (1994) 747–758 50. : Removing noise and preserving details with relaxed median filters. Journal of Mathematical Imaging and Vision 11(2) (1999) 161–177 51. : Image denoising: A nonlinear robust statistical approach. IEEE Transactions on Signal Processing 49(12) (2001) 3045–3054 52. : Denoising of electron tomographic reconstructions using multiscale transformations.
Various linear or nonlinear functions can be used here to stretch the contrast profile. We build a transfer function which consists of two pieces: a convex curve in the dark-intensity range and a concave curve in the bright-intensity range. The overall function is C1 continuous. Finally, we map the intensity of each voxel to a new one using the calculated transfer function. The performance of this algorithm is shown in Fig. 3. (B) Filtering: The input images are often contaminated with noise and are therefore need to be filtered.
2. 3). 1 Image Processing (A) Contrast Enhancement: The three dimensional intensity data often possesses a low contrast between structural features and the background, thereby making further processing all the more difficult. Image contrast enhancement is a process used to improve the image quality for better visual appearance for subsequent operations. The most commonly used methods utilize global contrast manipulation based on global [31, 32] or local histogram equalization [31–34], retinex model [35, 36] and wavelet decomposition [37, 38].
Advances in computational vision and medical image processing by Tavares J., Jorge R. (eds.)