An Accurate Liver Segmentation Method Using Parallel Computing Algorithm
DOI:
https://doi.org/10.14738/jbemi.23.1187Keywords:
Text classification, Semantic Web with weighted idf feature, Expanded query, Fuzzy Semantic Web, Fuzzy Ranking Algorithm.Abstract
In liver, separating touching objects in an image is one of the more difficult image processing operations. Because of the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. Thus, with using segmentation based algorithm, choice of appropriate segmentation technique type for each circumstance becomes an essential task. The watershed transform is often applied to this problem. The watershed transform finds "catchment basins" and "watershed ridge lines" in an image by treating it as a surface where light pixels are high and dark pixels are low. Segmentation using the watershed transform works well if one can identify, or "mark," foreground objects and background locations. This algorithm was done on twenty-five patients. A watershed transform Algorithm liver segmentation method was proposed in this study. Proposed method is able to determine the liver boundaries accurately. It is able to segment liver and improves radiological analysis and diagnosis.
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