We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to...
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We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.
A single click ensemble segmentation (SCES) approach based on an existing "Click & Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple...
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A single click ensemble segmentation (SCES) approach based on an existing "Click & Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated. (C) 2012 Elsevier Ltd. All rights reserved.
A novel segmentation algorithm for the detection of retinal vessels in funduscopic images is proposed, in which the benefits of both supervised and unsupervised methods are exploited. ensemble learning based segmentat...
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ISBN:
(纸本)9781424441266
A novel segmentation algorithm for the detection of retinal vessels in funduscopic images is proposed, in which the benefits of both supervised and unsupervised methods are exploited. ensemble learning based segmentation (ELBS) is employed for the segmentation of large and medium sized vessels, after which a local curve fitting technique is used for the detection of the thin retinal vessels. The general ELBS algorithm is modified to boost performance by the incorporation of specific knowledge of false negative segmentation result areas. Curve fitting is based on a two-hypotheses polynomial regression and is capable of automatically removing outliers from a point cloud. Evaluation on the DRIVE database compared the presented method favorably to previously published algorithms. Sensitivity and specificity were 0.8854 and 0.9363.
Due to the high diversity of image data, image segmentation is still a very challenging problem after decades of development. Each segmentation algorithm has its merits as well as its drawbacks. Instead of segmenting ...
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Due to the high diversity of image data, image segmentation is still a very challenging problem after decades of development. Each segmentation algorithm has its merits as well as its drawbacks. Instead of segmenting images via conventional techniques, inspired by the idea of the ensemble clustering technique that combines a set of weak clusterers to obtain a strong clusterer, we propose to achieve a consensus segmentation by fusing evidence accumulated from multiple weak segmentations (or over segmentations). We present a novel image segmentation approach which exploits multiple over segmentations and achieves segmentation results by hierarchical region merging. The cross-region evidence accumulation (CREA) mechanism is designed for collecting information among over segmentations. The pixel-pairs across regions are treated as a bag of independent voters and the cumulative votes from multiple over-segmentations are fused to estimate the coherency of adjacent regions. We further integrate the brightness, color, and texture cues for measuring the appearance similarity between regions in an over-segmentation, which, together with the CREA information, are utilized for making the region merging decisions. Experiments are conducted on multiple public data sets, which demonstrate the superiority of our-approach in terms of both effectiveness and efficiency when compared to the state-of-the-art. (C) 2016 Elsevier B.V. All rights reserved.
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