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|Title:||A clustering based transfer function for volume rendering using gray-gradient mode histogram||Authors:||Lan, Yisha
Ng, Eddie Yin Kwee
|Keywords:||Gray-gradient Mode Histogram
|Issue Date:||2019||Source:||Lan, Y., Ding, Y., Luo, X., Zhang, Y., Huang, C., Ng, E. Y. K., . . . Che, W. (2019). A clustering based transfer function for volume rendering using gray-gradient mode histogram. IEEE Access, 7, 80737-80747. doi:10.1109/ACCESS.2019.2923080||Series/Report no.:||IEEE Access||Abstract:||Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image.||URI:||https://hdl.handle.net/10356/83534
|DOI:||http://dx.doi.org/10.1109/ACCESS.2019.2923080||Rights:||© 2019 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license*, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.||metadata.item.grantfulltext:||open||metadata.item.fulltext:||With Fulltext|
|Appears in Collections:||MAE Journal Articles|
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