Mathematical Morphology and Rough Sets

In this paper we extend some previously established links between the derivation operators used in formal concept analysis and some mathematical morphology operators to fuzzy concept analysis. We also propose to use mathematical morphology to navigate in a fuzzy concept lattice and perform operations on it. Links with other lattice-based for malisms such as rough sets and F-transforms are also established. This paper proposes a discussion and new results on such links and their potential interest.

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Intelligent Tool Condition Monitoring System Based on Rough Sets and Mathematical Morphology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.10-12.722 ◽

2007 ◽

Vol 10-12 ◽

pp. 722-726 ◽

Cited By ~ 4

Author(s):

Li Zhang ◽

Shi Ming Ji ◽

Yi Xie ◽

Qiao Ling Yuan ◽

Yin Dong Zhang ◽

...

Keyword(s):

Tool Wear ◽

Mathematical Morphology ◽

Condition Monitoring ◽

Rough Sets ◽

Cutting Tool ◽

Cutting Tools ◽

Tool Condition Monitoring ◽

Online Tool ◽

Tool Condition ◽

Condition Monitoring System

The image of cutting tools provides reliable information regarding the extent of tool wear. In this paper, we propose the theory of image processing based on rough sets and mathematical morphology to analyzing the flank faces which are chosen as our monitoring object. First, through plotting the appropriate subset, the rough sets filter is used to enhancement the image of tool wear. Then, the mathematical morphology theory is applied to process the translated binary image. Finally, tool condition monitoring is realized by measuring the area of tool wear. This paper gives the corresponding monitoring principal and proposes a new algorithm to process the cutting tool image. The algorithm is also flexible and fast enough to be implemented in real time for online tool wear or tool condition monitoring.

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ROUGH MORPHOLOGY HYBRID APPROACH FOR MAMMOGRAPHY IMAGE CLASSIFICATION AND PREDICTION

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026808002181 ◽

2008 ◽

Vol 07 (01) ◽

pp. 17-42 ◽

Cited By ~ 4

Author(s):

ABOUL ELLA HASSANIEN ◽

AJITH ABRAHAM

Keyword(s):

Mathematical Morphology ◽

Rough Sets ◽

Attribute Reduction ◽

Hybrid Approach ◽

Region Of Interest ◽

Overall Performance ◽

Mammography Image ◽

Occurrence Matrix ◽

Mammogram Images

The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant and benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest (ROI) and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the ROI by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different ROI to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

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