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7 Conclusions Barcode barcode library for net generate, create barcode none for With this in mind, we are proposing a new fuzzy membership function that transforms the intensity of a pixel into the uzzy domain, according to the direction of the brightness slope in its neighboring transition zone A new intensification operator based on a polygon is introduced for determining the corrected intensity value for any pixel The membership fuzzification classifier dynamically evaluates every pixel s brightness by optimizing its contrast according to the neighboring pixels The method needs no preprocessing or training and does not change the brightness nature of the segmented image compared to the original image The DFC method has been tested on a medical mammography database and has been shown to be effective for detecting abnormal breast regions.

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In comparisons with the traditional edge detection techniques, our current DFC method shows significant abnormality details, where many other methods (eg Kirsch, Laplacian) revealed irrelevant edges as well as extra noise For Sobel and Prewitt, the original image becomes completely black With contrast enhancements, Sobel and Prewitt still show extra edges and noise However, with simpler edge detection techniques like the Cafforio method [10], the result is completely filled with noise Moreover, we believe that our DFC technique can be used to generate association rules for mining abnormal mammograms This will be left to our future research work Finally, we are currently involved in developing global measures for measuring the coherence of our DFC method in comparison with the other techniques such as Canny or Gabor GEF filters This will enable us quantitatively to determine the quality of the developed technique.

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