Radiometric homogenization of the color cryosection images from the VHP Lungs for 3D segmentation of blood vessels

Márquez, Juan Francisco; Schmitt, Francis

doi:10.1016/s0895-6111(00)00018-5

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Image preprocessing plays an important role in obtaining an accurate classification result. First, in order to enhance the image contrast, the image dataset is processed by adaptive histogram homogenization [23] . At the same time, the bilinear interpolation algorithm is adopted to maintain uniformity.…”

Section: Methodologiesmentioning

confidence: 99%

Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Cui

et al. 2022

Biomedical Signal Processing and Control

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Section: Methodologiesmentioning

confidence: 99%

Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Cui

et al. 2022

Biomedical Signal Processing and Control

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“…Next, we applied a local, adaptive, homogenization process, specific to the characteristic alterations found in the VHP color images, and without filtering small details. Such procedure and gamma homogenization are detailed in [17]. In short, it consisted of obtaining local color histograms in a small neighborhood of each pixel in differential image slices (I n+1 -I n ) and then making a pixel-by-pixel color correction.…”

Section: Correction Of Regional Color Heterogeneities In the Vhp Imagesmentioning

confidence: 99%

“…As explained in Ref. [17], a correction was devised by adding, pixel-by-pixel, a fraction of the local, intensity-mode difference to slice I n . Such difference corresponds to the heterogeneity-induced shift of the local mode, with respect to the origin.…”

Section: Correction Of Regional Color Heterogeneities In the Vhp Imagesmentioning

confidence: 99%

Building a three-dimensional model of the upper gastrointestinal tract for computer simulations of swallowing

Gastélum

Mata

Brito-de-la-Fuente

et al. 2015

Med Biol Eng Comput

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We aimed to provide realistic three-dimensional (3D) models to be used in numerical simulations of peristaltic flow in patients exhibiting difficulty in swallowing, also known as dysphagia. To this end, a 3D model of the upper gastrointestinal tract was built from the color cryosection images of the Visible Human Project dataset. Regional color heterogeneities were corrected by centering local histograms of the image difference between slices. A voxel-based model was generated by stacking contours from the color images. A triangle mesh was built, smoothed and simplified. Visualization tools were developed for browsing the model at different stages and for virtual endoscopy navigation. As result, a computer model of the esophagus and the stomach was obtained, mainly for modeling swallowing disorders. A central-axis curve was also obtained for virtual navigation and to replicate conditions relevant to swallowing disorders modeling. We show renderings of the model and discuss its use for simulating swallowing as a function of bolus rheological properties. The information obtained from simulation studies with our model could be useful for physicians in selecting the correct nutritional emulsions for patients with dysphagia.

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“…Color information can be quantified by red (R), green (G), and blue (B) values that range from 0 to 255 in the RGB color space and are used as the spatial features for pixels. It should be noted that the color histogram [33] is an approximation of the probabilistic distribution of color. Figure 6(a) shows an example of color histograms for the segmented brain tissue image.…”

Section: Segmentation Of White Mattermentioning

confidence: 99%

Untitled

Zheng

Luo

et al. 2014

J. Med. Biol. Eng.

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Cryosection images contain fairly rich and original details of the brain anatomy. Accurate and fast segmentation of cryosection images is of great significance for research of the human brain and development of the Visible Human Project. However, most automated approaches in the literature are designed for magnetic resonance imaging or computed tomography data, and they may not be suitable for cryosection images. Cryosection image segmentation is often realized manually or semi-automatically in practice. The present study proposes an automated algorithm for cryosection image segmentation of brain tissue and white matter and evaluates its accuracy using the Chinese Visible Human (CVH) dataset. This method combines a mathematical morphological approach to delineate brain tissue and k-means clustering to uniquely identify white matter. Firstly, the region of brain tissue is detected coarsely using connected component labeling combined with morphological reconstruction. Then, morphological operations are used for final boundary determination to complete the segmentation of brain tissue. Finally, k-means clustering is employed to extract white matter based on segmented brain tissue. The algorithm was applied to the CVH dataset to automatically extract the entire brain tissue and white matter in the cerebrum, cerebellum, and brain stem. Additionally, the proposed mathematical morphological approach is compared with the region growing method and the threshold morphological method for brain segmentation, and the k-means clustering method is compared with the fuzzy c-means clustering algorithm and the Gaussian mixture model coupled with the expectation-maximization algorithm for white matter extraction. To evaluate performance, a quantitative analysis was conducted using the dice similarity index, falsepositive dice, and false-negative dice for comparison with manually obtained segmentation results produced by anatomy experts. Results indicate that the proposed algorithm is capable of accurate segmentation and substantial agreement with the gold standard.

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Radiometric homogenization of the color cryosection images from the VHP Lungs for 3D segmentation of blood vessels

Cited by 5 publications

References 11 publications

Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Building a three-dimensional model of the upper gastrointestinal tract for computer simulations of swallowing

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