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- W2898495963 abstract "Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. This paper presents a parallel approach for fast and robust object detection in a medical image. First, the proposed approach consists to decompose the image into multiple resolutions by a Gaussian pyramid algorithm. Then, the object detection in the higher pyramids levels is done in parallel by a Hybrid model combining Watershed algorithm, GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models where the initial contour is subdivided into sub-contours, which are independent from each other. Each sub-contour converges independently in parallel. The last step of our approach consists to project the sub-contours detected in the low resolution image to the high-resolution image. The experimental results were performed using a number of synthetic and medical images. Its rapidity is justified by runtime comparison with a conventional method." @default.
- W2898495963 created "2018-11-02" @default.
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- W2898495963 date "2018-10-25" @default.
- W2898495963 modified "2023-09-30" @default.
- W2898495963 title "A New Parallel Method for Medical Image Segmentation Using Watershed Algorithm and an Improved Gradient Vector Flow" @default.
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- W2898495963 doi "https://doi.org/10.1007/978-3-030-03577-8_70" @default.
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