Literature DB >> 20821141

Radial-searching contour extraction method based on a modified active contour model for mammographic masses.

Toshiaki Nakagawa1, Takeshi Hara, Hiroshi Fujita, Katsuhei Horita, Takuji Iwase, Tokiko Endo.   

Abstract

In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images-an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram-for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several "difficult contours" on 53 digitized mammograms. The extraction results were compared quantitatively with the "correct segmentation" represented by an experienced physician's sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes.

Mesh:

Year:  2008        PMID: 20821141     DOI: 10.1007/s12194-008-0022-5

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  9 in total

1.  Segmentation of suspicious densities in digital mammograms.

Authors:  G M te Brake; N Karssemeijer
Journal:  Med Phys       Date:  2001-02       Impact factor: 4.071

2.  Development of an automated method for detecting mammographic masses with a partial loss of region.

Authors:  Y Hatanaka; T Hara; H Fujita; S Kasai; T Endo; T Iwase
Journal:  IEEE Trans Med Imaging       Date:  2001-12       Impact factor: 10.048

3.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Authors:  B Sahiner; N Petrick; H P Chan; L M Hadjiiski; C Paramagul; M A Helvie; M N Gurcan
Journal:  IEEE Trans Med Imaging       Date:  2001-12       Impact factor: 10.048

4.  Computerized radiographic mass detection--part I: Lesion site selection by morphological enhancement and contextual segmentation.

Authors:  H Li; Y Wang; K J Liu; S C Lo; M T Freedman
Journal:  IEEE Trans Med Imaging       Date:  2001-04       Impact factor: 10.048

5.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

Authors:  Sheila Timp; Nico Karssemeijer
Journal:  Med Phys       Date:  2004-05       Impact factor: 4.071

6.  Automated detection of clustered microcalcifications on mammograms: CAD system application to MIAS database.

Authors:  N Ibrahim; H Fujita; T Hara; T Endo
Journal:  Phys Med Biol       Date:  1997-12       Impact factor: 3.609

7.  Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification.

Authors:  N Petrick; H P Chan; D Wei; B Sahiner; M A Helvie; D D Adler
Journal:  Med Phys       Date:  1996-10       Impact factor: 4.071

8.  Automated seeded lesion segmentation on digital mammograms.

Authors:  M A Kupinski; M L Giger
Journal:  IEEE Trans Med Imaging       Date:  1998-08       Impact factor: 10.048

9.  Markov random field for tumor detection in digital mammography.

Authors:  H D Li; M Kallergi; L P Clarke; V K Jain; R A Clark
Journal:  IEEE Trans Med Imaging       Date:  1995       Impact factor: 10.048

  9 in total

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