Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing

被引:135
|
作者
Mencattini, Arianna [1 ]
Salmeri, Marcello [1 ]
Lojacono, Roberto [1 ]
Frigerio, Manuela [1 ]
Caselli, Federica [2 ]
机构
[1] Univ Roma Tor Vergata, Dept Elect Engn, I-00133 Rome, Italy
[2] Univ Roma Tor Vergata, Dept Civil Engn, I-00133 Rome, Italy
关键词
dyadic wavelet transform; image enhancement and denoising; mass detection; microcalcification detection;
D O I
10.1109/TIM.2007.915470
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.
引用
收藏
页码:1422 / 1430
页数:9
相关论文
共 50 条
  • [21] Interpretable breast cancer classification using CNNs on mammographic images
    Balve, Ann-Kristin
    Hendrix, Peter
    CONFERENCE ON HEALTH, INFERENCE, AND LEARNING, 2024, 248 : 410 - 426
  • [23] Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images
    Almalki, Yassir Edrees
    Shaf, Ahmad
    Ali, Tariq
    Aamir, Muhammad
    Alduraibi, Sharifa Khalid
    Almutiri, Shoayea Mohessen
    Irfan, Muhammad
    Basha, Mohammad Abd Alkhalik
    Alduraibi, Alaa Khalid
    Alamri, Abdulrahman Manaa
    Azam, Muhammad Zeeshan
    Alshamrani, Khalaf
    Alshamrani, Hassan A.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 72 (03): : 4833 - 4851
  • [24] Development of Computer-Aided Detection of Breast Lesion Using Gabor-Wavelet BASED Features in Mammographic Images
    Yousefi, Bardia
    Ting, Hua-Nong
    Mirhassani, Seyed Mostafa
    Hosseini, Mohammadmehdi
    2013 IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE 2013), 2013, : 127 - +
  • [25] MAMMOGRAPHIC IMAGE-PROCESSING USING WAVELET PROCESSING TECHNIQUES
    LAINE, A
    HUDA, W
    STEINBACH, BG
    HONEYMAN, JC
    EUROPEAN RADIOLOGY, 1995, 5 (05) : 518 - 523
  • [26] Denoising CT Images using wavelet transform
    Gabralla, Lubna
    Mahersia, Hela
    Zaroug, Marwan
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2015, 6 (05) : 125 - 129
  • [27] Denoising of multispectral images using wavelet thresholding
    Scheunders, P
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING IX, 2004, 5238 : 28 - 35
  • [28] Fuzzifying Images using Fuzzy Wavelet Denoising
    Palma, Giovanni
    Bloch, Isabelle
    Muller, Serge
    Iordache, Razvan
    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, 2009, : 135 - 140
  • [29] Gray level clustering and contrast enhancement (GLC–CE) of mammographic breast cancer images
    Bhagwati Charan Patel
    G. R. Sinha
    CSI Transactions on ICT, 2015, 2 (4) : 279 - 286
  • [30] Performance Analysis of Mammographic Image Enhancement Techniques for Early Detection of Breast Cancer
    Singh, Shailaja
    Yadav, Anamika
    Singh, Bikesh Kumar
    ADVANCES IN PARALLEL, DISTRIBUTED COMPUTING, 2011, 203 : 439 - +