Literature DB >> 18346931

Automatic initialization of an active shape model of the prostate.

F Arámbula Cosío1.   

Abstract

In this work is reported a new method for automatic segmentation of the boundary of the prostate, in transurethral ultrasound images. The scheme is based on a robust automatic initialization of an active shape model (ASM) of the prostate, which is subsequently fitted to the boundary of the gland. The initialization of the ASM is based on pixel classification to estimate the prostate region in an ultrasound image, followed by automatic adjustment - using a multipopulation genetic algorithm (MPGA) - of the initial pose of the ASM to the binary image produced by the classifier. The initial pose is next adjusted to the gray level ultrasound image, using the MPGA. After automatic initialization, the ASM is adjusted to the gray level ultrasound image to produce the final prostate contour. The method provides fast and robust segmentation of the prostate boundary. Validation results on 22 ultrasound images are reported with 1.74 mm of mean boundary error and an estimated processing time of 66 per image. Our automatic initialization method can be applied with the ASMs of different organs in various imaging modalities.

Mesh:

Year:  2008        PMID: 18346931     DOI: 10.1016/j.media.2008.02.001

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  14 in total

1.  A Learning based Hierarchical Framework for Automatic Prostate Localization in CT Images.

Authors:  Shu Liao; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

2.  Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning.

Authors:  Najeeb Chowdhury; Robert Toth; Jonathan Chappelow; Sung Kim; Sabin Motwani; Salman Punekar; Haibo Lin; Stefan Both; Neha Vapiwala; Stephen Hahn; Anant Madabhushi
Journal:  Med Phys       Date:  2012-04       Impact factor: 4.071

3.  Prostate segmentation in HIFU therapy.

Authors:  Carole Garnier; Jean-Jacques Bellanger; Ke Wu; Huazhong Shu; Nathalie Costet; Romain Mathieu; Renaud de Crevoisier; Jean-Louis Coatrieux
Journal:  IEEE Trans Med Imaging       Date:  2010-11-29       Impact factor: 10.048

4.  A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation.

Authors:  Robert Toth; Pallavi Tiwari; Mark Rosen; Galen Reed; John Kurhanewicz; Arjun Kalyanpur; Sona Pungavkar; Anant Madabhushi
Journal:  Med Image Anal       Date:  2010-10-28       Impact factor: 8.545

5.  Segmenting CT prostate images using population and patient-specific statistics for radiotherapy.

Authors:  Qianjin Feng; Mark Foskey; Wufan Chen; Dinggang Shen
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

6.  A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning.

Authors:  Awais Mansoor; Juan J Cerrolaza; Geovanny Perez; Elijah Biggs; Kazunori Okada; Gustavo Nino; Marius George Linguraru
Journal:  IEEE Trans Biomed Eng       Date:  2019-08-14       Impact factor: 4.538

7.  Statistical shape and texture model of quadrature phase information for prostate segmentation.

Authors:  Soumya Ghose; Arnau Oliver; Robert Martí; Xavier Lladó; Jordi Freixenet; Jhimli Mitra; Joan C Vilanova; Josep Comet-Batlle; Fabrice Meriaudeau
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-06-01       Impact factor: 2.924

8.  Prostate multimodality image registration based on B-splines and quadrature local energy.

Authors:  Jhimli Mitra; Robert Martí; Arnau Oliver; Xavier Lladó; Soumya Ghose; Joan C Vilanova; Fabrice Meriaudeau
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-06-26       Impact factor: 2.924

9.  Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model.

Authors:  Benjamín Gutiérrez-Becker; Fernando Arámbula Cosío; Mario E Guzmán Huerta; Jesús Andrés Benavides-Serralde; Lisbeth Camargo-Marín; Verónica Medina Bañuelos
Journal:  Med Biol Eng Comput       Date:  2013-05-18       Impact factor: 2.602

10.  Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

Authors:  Awais Mansoor; Juan J Cerrolaza; Geovanny Perez; Elijah Biggs; Gustavo Nino; Marius George Linguraru
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-02-24
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.