Literature DB >> 12105730

A segmentation technique to detect masses in dense breast digitized mammograms.

Vivian Toledo Santos1, Homero Schiabel, Cláudio E Góes, Rodrigo H Benatti.   

Abstract

This article describes a segmentation technique that uses the watershed transformation to provide mass detection in dense breast digitized images. The technique consists of four steps: preprocessing, which isolates the breast from the image background; histogram equalization to enhance the contrast; watershed transformation, which calculates the gradient for the whole image; and a topography technique that organizes the segmented parts. We processed 109 regions of interest extracted from mammograms. Preliminary results showed 20% false positive and 85.4% true positive detection regarding the equalized images containing masses. Good results were obtained with this technique. However, additional techniques should be developed to eliminate noise in some images. This technique could be an important tool in mammography.

Mesh:

Year:  2002        PMID: 12105730     DOI: 10.1007/s10278-002-5071-4

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  2 in total

1.  Experimental hip fracture load can be predicted from plain radiography by combined analysis of trabecular bone structure and bone geometry.

Authors:  P Pulkkinen; T Jämsä; E-M Lochmüller; V Kuhn; M T Nieminen; F Eckstein
Journal:  Osteoporos Int       Date:  2007-09-22       Impact factor: 4.507

2.  Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Authors:  Jinjin Hai; Kai Qiao; Jian Chen; Hongna Tan; Jingbo Xu; Lei Zeng; Dapeng Shi; Bin Yan
Journal:  J Healthc Eng       Date:  2019-01-14       Impact factor: 2.682

  2 in total

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