Literature DB >> 16279178

Toward a generic evaluation of image segmentation.

Jaime S Cardoso1, Luís Corte-Real.   

Abstract

Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.

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Year:  2005        PMID: 16279178     DOI: 10.1109/tip.2005.854491

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  8 in total

1.  Balancing the Role of Priors in Multi-Observer Segmentation Evaluation.

Authors:  Yaoyao Zhu; Xiaolei Huang; Wei Wang; Daniel Lopresti; Rodney Long; Sameer Antani; Zhiyun Xue; George Thoma
Journal:  J Signal Process Syst       Date:  2008-05-28

2.  Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Authors:  Jiantao Pu; Justus Roos; Chin A Yi; Sandy Napel; Geoffrey D Rubin; David S Paik
Journal:  Comput Med Imaging Graph       Date:  2008-06-02       Impact factor: 4.790

3.  Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions.

Authors:  M A Deeley; A Chen; R D Datteri; J Noble; A Cmelak; E Donnelly; A Malcolm; L Moretti; J Jaboin; K Niermann; Eddy S Yang; David S Yu; B M Dawant
Journal:  Phys Med Biol       Date:  2013-05-17       Impact factor: 3.609

4.  A method for the evaluation of thousands of automated 3D stem cell segmentations.

Authors:  P Bajcsy; M Simon; S J Florczyk; C G Simon; D Juba; M C Brady
Journal:  J Microsc       Date:  2015-08-13       Impact factor: 1.758

5.  Flexible methods for segmentation evaluation: results from CT-based luggage screening.

Authors:  Seemeen Karimi; Xiaoqian Jiang; Pamela Cosman; Harry Martz
Journal:  J Xray Sci Technol       Date:  2014       Impact factor: 1.535

6.  Performance of a simple chromatin-rich segmentation algorithm in quantifying basal cell carcinoma from histology images.

Authors:  Kyle Lesack; Christopher Naugler
Journal:  BMC Res Notes       Date:  2012-01-17

7.  Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging.

Authors:  Saket Navlakha; Parvez Ahammad; Eugene W Myers
Journal:  BMC Bioinformatics       Date:  2013-10-04       Impact factor: 3.169

8.  A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images.

Authors:  Han Gao; Yunwei Tang; Linhai Jing; Hui Li; Haifeng Ding
Journal:  Sensors (Basel)       Date:  2017-10-24       Impact factor: 3.576

  8 in total

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