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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    ENHANCED NOISE FILTERING AND SEGMENTATION USING HCLAHE AND ADF WITH GACT TO IDENTIFY TUMOR CELLS
    Sujitha Priya D, Dr. V. Radha
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1528-1552 . 

    Abstract

    The segmentation of mammogram images is an essential component in breast cancer early detection. Accurate segmentation is still difficult to achieve because of the mammography images' intrinsic complexity and fluctuation. Noise frequently degrades mammogram pictures, making it difficult to detect and segment tumor cells accurately. Using evolutionary algorithms to segment images and HCLAHE with wavelet denoising to reduce noise, we provide a unique method in this work for the detection of tumor cells. Wavelet denoising is used in the initial stage to take the noise out of the mammography images. In the second stage, contourlet transform genetic algorithms-based image segmentation is used to precisely detect and separate tumor spots. While the genetic algorithm optimizes the segmentation process's parameters, the contourlet transform offers a multiresolution examination of the image that improves the mammogram's edges and features. The findings show that the proposed method outperforms cutting-edge techniques in terms of segmentation accuracy, sensitivity, and specificity. The suggested strategy has the potential to be used in clinical practice to improve the accuracy of tumor cell detection and reduce false positives and false negatives.

    Keyword

    Breast Cancer, Mammogram, Noise Filtering, Image Segmentation, HCLAHE, Genetic Algorithms, Contourlet Transform.


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ISSN 1004-9037

         

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