DUHO image segmentation based on unseeded region growing on superpixels

Duong, Trung H.; Hoberock, L. L.

doi:10.1109/ccwc.2018.8301760

Cited by 2 publications

(4 citation statements)

References 22 publications

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“…Paper [12] shows a modified region growing based on the merging superpixels. A superpixel is a group of pixels combined by a certain feature.…”

Section: Related Workmentioning

confidence: 99%

“…The complexity of such algorithms is O(n 2 ). A simple linear iterative clustering (SLIC) algorithm is used in studies [12], [20], and [9]. Initially, this method was presented in paper [1], where Radhakrishna Achanta developed a superpixel-based segmentation method using k-means clustering for a fivedimensional feature space.…”

Section: Related Workmentioning

confidence: 99%

“…The main difference between the proposed algorithm and the methods reflected in papers [12,28] is that the proposed algorithm does not analyze single pixels belonging to superpixels. For instance, the standard region growing algorithm processes all 100 pixels of a 10x10 superpixel.…”

Section: Fig 3 the Workflow Of The Square Blocks Propagation Algorithmmentioning

confidence: 99%

“…It should also be noted that the initial square size depends on the segmented area and the image resolution. For initial square size K we used the method presented in paper [12], where parameter K is calculated as follows:…”

Section: Size Of Square Blocksmentioning

confidence: 99%

See 3 more Smart Citations

Segmentation Algorithm Based on Square Blocks Propagation

Danilov

Скирневский

Skirnevskiy

et al. 2019

GraphiCon'2019 Proceedings. Volume 2

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This research is devoted to the segmentation of heart and brain anatomical structures. In the study, we present a segmentation algorithm based on the square blocks (superpixels) propagation. The square blocks propagation algorithm checks two criteria. For the first criteria, the current intensity of the pixel is compared to the average intensity of the segmented region. For the second criterion, the intensity difference of the pixels lying on the superpixel sides is compared to the threshold. Once these criteria are successfully checked, the algorithm merges homogeneous superpixels into one region. Then the following superpixels are attached to the final superpixel set. The last step of the proposed method is the spline generation. The spline delineates the borders of the region of interest. The main parameter of the algorithm is the size of a square block. The cardiac MRI dataset of the University of York and the brain tumor dataset of Southern Medical University were used to estimate the segmentation accuracy and processing time. The highest Dice similarity coefficients obtained by the presented algorithm for the left ventricle and the brain tumor are 0.93±0.03 and 0.89±0.07 respectively. One of the most important features of the border detection step is its scalability. It allows implementing different one-dimensional methods for border detection.

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“…Paper [12] shows a modified region growing based on the merging superpixels. A superpixel is a group of pixels combined by a certain feature.…”

Section: Related Workmentioning

confidence: 99%