Authors:
Songlin Du
;
Yaping Yan
and
Yide Ma
Affiliation:
Lanzhou University, China
Keyword(s):
Breast Mass Segmentation, Pulse-Coupled Neural Network, Distance Regularized Level Set Evolution.
Related
Ontology
Subjects/Areas/Topics:
Bioimaging
;
Biomedical Engineering
;
Image Processing Methods
;
Medical Imaging and Diagnosis
Abstract:
Motivation: Computer-aided diagnosis (CAD) is an important means for the clinical detection of breast cancer.
Mass is a common manifestation of breast cancer. This work aims to develop an effective breast mass segmentation
algorithm for CAD systems. Method: On one hand, pulse-coupled neural network (PCNN) and level set
(LS) method have complementary advantages in image segmentation, we therefore combine PCNN and LS.
On the other hand, traditional LS method formulates the evolution of the contour through the evolution of a
level set function (LSF), and LSF typically develops irregularities during its evolution, which may cause numerical
errors and eventually destroy the stability of the evolution. So we use an improved LS model, named
distance regularized level set evolution (DRLSE), to achieve desirable segmentation performance. Specifically,
we extract the region of interest (ROI) with PCNN and sets initial contour for DRLSE first. Then the
finely segmentation is achieved by DRLSE.
Results: Both qualitative and quantitative experiments on three
large-scale mammography databases prove that the proposed method achieves high segmentation accuracy.
Conclusion: The proposed algorithm is effective for automatic breast mass segmentation. Significance: First,
the sketchy position of mass is fixed by PCNN, which guides the algorithm to define a flexibly initial contour
for DRLSE. This strategy makes it easier for the contour to move from initial position towards the boundary
between mass and normal tissue. Second, the use of DRLSE, which introduces an intrinsic capability of
maintaining regularity of the LSF, ensures stable LS evolution and achieves accurate segmentation.
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