A semisupervised fuzzy GrowCut algorithm for segmenting masses of regions of interest of mammography images

A semisupervised fuzzy GrowCut algorithm for segmenting masses of regions of interest of mammography images

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According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. This chapter shows an adaptive semisupervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model nondefined borders. In this proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated the proposal using 59 lesion images obtained from MiniMIAS database. The results were compared with the semisupervised state-of-the-art approaches bidimensional empirical mode decomposition, breast mass contour segmentation, wavelet analysis, topographic approach, and marker-controlled watershed (MCW). The results show that fuzzy GrowCut achieves better results for circumscribed, spiculated lesions, and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.

Chapter Contents:

  • Abstract
  • 3.1 Introduction
  • 3.2 Related work
  • 3.3 Materials and methods
  • 3.3.1 Fuzzy GrowCut model
  • 3.3.2 Automatic selection of seeds
  • 3.3.3 Adaptive selection of parameters
  • 3.3.4 Methodology
  • 3.3.5 Experimental environment
  • 3.3.6 Metrics
  • 3.4 Results
  • 3.4.1 Fault tolerance analysis
  • 3.4.2 General results
  • 3.5 Conclusion
  • References

Inspec keywords: image segmentation; fuzzy set theory; mammography; evolutionary computation; cancer; biological organs; Gaussian processes; cellular automata; medical image processing

Other keywords: MiniMIAS database; breast cancer diagnosis; mammography image analysis; ground-truth images; shape analysis; differential evolution algorithm; spiculated lesions; internal seed points; mammary lesions; automaton evolution rule; ill-defined lesions; Gaussian fuzzy membership function; external seed points; cellular automata; suspicious lesion; lesion images; World Health Organization; manual selection; adaptive semisupervised version; adequate selection; general-purpose segmentation method; circumscribed lesions; semisupervised fuzzy GrowCut algorithm; effective imaging technology; texture

Subjects: Other topics in statistics; Optimisation techniques; Optical, image and video signal processing; Combinatorial mathematics; Optimisation techniques; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Other topics in statistics; Combinatorial mathematics; Patient diagnostic methods and instrumentation; Probability theory, stochastic processes, and statistics; Algebra, set theory, and graph theory; X-rays and particle beams (medical uses); Computer vision and image processing techniques; Biology and medical computing

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