Literature DB >> 18296257

Adaptive image region-growing.

Y L Chang1, X Li.   

Abstract

Proposes a simple, yet general and powerful, region-growing framework for image segmentation. The region-growing process is guided by regional feature analysis; no parameter tuning or a priori knowledge about the image is required. To decide if two regions should be merged, instead of comparing the difference of region feature means with a predefined threshold, the authors adaptively assess region homogeneity from region feature distributions. This results in an algorithm that is robust with respect to various image characteristics. The merge criterion also minimizes the number of merge rejections and results in a fast region-growing process that is amenable to parallelization.

Year:  1994        PMID: 18296257     DOI: 10.1109/83.336259

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


  14 in total

1.  A new method for temperature-field reconstruction during ultrasound-monitored cryosurgery using potential-field analogy.

Authors:  Chandrajit Thaokar; Michael R Rossi; Yoed Rabin
Journal:  Cryobiology       Date:  2015-11-14       Impact factor: 2.487

2.  Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments.

Authors:  Leonardo Rundo; Carmelo Militello; Salvatore Vitabile; Carlo Casarino; Giorgio Russo; Massimo Midiri; Maria Carla Gilardi
Journal:  Med Biol Eng Comput       Date:  2015-11-03       Impact factor: 2.602

3.  Experimental verification of numerical simulations of cryosurgery with application to computerized planning.

Authors:  Michael R Rossi; Yoed Rabin
Journal:  Phys Med Biol       Date:  2007-07-03       Impact factor: 3.609

4.  Left-ventricle boundary detection from nuclear medicine images.

Authors:  X Dai; W E Snyder; G L Bilbro; R Williams; R Cowan
Journal:  J Digit Imaging       Date:  1998-02       Impact factor: 4.056

5.  Variabilities in Reference Standard by Radiologists and Performance Assessment in Detection of Pulmonary Embolism in CT Pulmonary Angiography.

Authors:  Chuan Zhou; Heang-Ping Chan; Aamer Chughtai; Smita Patel; Jean Kuriakose; Lubomir M Hadjiiski; Jun Wei; Ella A Kazerooni
Journal:  J Digit Imaging       Date:  2019-12       Impact factor: 4.056

6.  Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Authors:  Xiaoming Liu; Shuxu Guo; Bingtao Yang; Shuzhi Ma; Huimao Zhang; Jing Li; Changjian Sun; Lanyi Jin; Xueyan Li; Qi Yang; Yu Fu
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

7.  Deep learning image segmentation reveals patterns of UV reflectance evolution in passerine birds.

Authors:  Yichen He; Zoë K Varley; Lara O Nouri; Christopher J A Moody; Michael D Jardine; Steve Maddock; Gavin H Thomas; Christopher R Cooney
Journal:  Nat Commun       Date:  2022-08-29       Impact factor: 17.694

8.  GC-ASM: Synergistic Integration of Graph-Cut and Active Shape Model Strategies for Medical Image Segmentation.

Authors:  Xinjian Chen; Jayaram K Udupa; Abass Alavi; Drew A Torigian
Journal:  Comput Vis Image Underst       Date:  2013-05       Impact factor: 3.876

9.  Statistical, Morphometric, Anatomical Shape Model (Atlas) of Calcaneus.

Authors:  Aleksandra U Melinska; Patryk Romaszkiewicz; Justyna Wagel; Marek Sasiadek; D Robert Iskander
Journal:  PLoS One       Date:  2015-08-13       Impact factor: 3.240

10.  Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets.

Authors:  Chuan Zhou; Heang-Ping Chan; Berkman Sahiner; Lubomir M Hadjiiski; Aamer Chughtai; Smita Patel; Jun Wei; Philip N Cascade; Ella A Kazerooni
Journal:  Med Phys       Date:  2009-08       Impact factor: 4.071

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