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- W4386004663 abstract "The most frequent type of cancer among women is breast cancer. Patients with breast cancer have a considerably higher chance of survival when they receive early identification and treatment. The greatest technique for early breast cancer detection is screening mammography analysis. Because cancerous tissues and glands are close to the illumination, the results of analysis by radiologists are frequently limited; therefore, using computer analysis is more convenient. Image thresholding is one of the simplest methods for separating tumors; however, the results are only good if the mammograms have high contrast and the threshold value is correctly chosen. In this article, we present an approach for increasing image contrast, while preserving tumor edges by combining the stationary wavelet transform with morphological transforms. The tumor is then extracted using the adaptive multi-threshold. By using performance evaluation criteria for segmentation methods, such as accuracy, sensitivity, specificity, and Dice similarity coefficient, the results are compared to the ground truth data, demonstrating the method's accuracy." @default.
- W4386004663 created "2023-08-20" @default.
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- W4386004663 date "2023-01-01" @default.
- W4386004663 modified "2023-10-12" @default.
- W4386004663 title "Detection of Abnormalities in Mammograms by Thresholding Based on Wavelet Transform and Morphological Operation" @default.
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- W4386004663 doi "https://doi.org/10.1007/978-981-99-4725-6_60" @default.
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