Literature DB >> 20529728

Snakules: a model-based active contour algorithm for the annotation of spicules on mammography.

Gautam S Muralidhar1, Alan C Bovik, J David Giese, Mehul P Sampat, Gary J Whitman, Tamara Miner Haygood, Tanya W Stephens, Mia K Markey.   

Abstract

We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the ability to deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Starting from a natural set of automatically detected candidate points, snakules are deployed in the region around a suspect spiculated mass location. Statistics of prior physical measurements of spiculated masses on mammography are used in the process of detecting the set of candidate points. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an image analysis technique to improve the specificity of CADe algorithms and as a CADe prompting tool.

Mesh:

Year:  2010        PMID: 20529728     DOI: 10.1109/TMI.2010.2052064

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  3 in total

1.  Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
Journal:  J Vis Exp       Date:  2013-08-30       Impact factor: 1.355

2.  Convolutional virtual electric field for image segmentation using active contours.

Authors:  Yuanquan Wang; Ce Zhu; Jiawan Zhang; Yuden Jian
Journal:  PLoS One       Date:  2014-10-31       Impact factor: 3.240

3.  High Precision Mammography Lesion Identification From Imprecise Medical Annotations.

Authors:  Ulzee An; Ankit Bhardwaj; Khader Shameer; Lakshminarayanan Subramanian
Journal:  Front Big Data       Date:  2021-12-03
  3 in total

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