Analysis of mammogram classification using a wavelet transform decomposition

Ferreira, Cristiane B. R.; Borges, Díbio Leandro

doi:10.1016/s0167-8655(02)00221-0

Cited by 83 publications

(27 citation statements)

References 4 publications

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“…A c c e p t e d M a n u s c r i p t Gabor Filter [20], Zernike Moments [21] and Wavelet Transform [22][23][24][25][26][27][28][29] are more popular than the others. In the classification as a final stage, suspicious regions are classified to two groups of benign or malign lesions.…”

Section: Page 4 Of 41mentioning

confidence: 99%

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Pak

Kanan

Alikhassi

2015

Computer Methods and Programs in Biomedicine

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Section: Page 4 Of 41mentioning

confidence: 99%

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Pak

Kanan

Alikhassi

2015

Computer Methods and Programs in Biomedicine

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“…These methods are usually composed of a wide range of combination of fuzzy logic, wavelet transformation or that of the neural network [2][3][4][5][6]. Since the mammograms show larger areas of varying contrast and brightness, thus the information is highly susceptible to being correlated [7][8][9][10]. Other researchers used wavelet transformation to an extent where it tends to give more consolidated results than the other methods [11,12]; therefore, the following study presents an effectively modeled algorithm for multi-wavelet transformation to denoise the noisy mammographic images to allow easy microclassification to help doctors or radiologist detect breast cancer easily.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transformation for Enhancing Mammographic Images

Rai

2017

Asian J Pharm Clin Res

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Objective: Mammographic images are often prone to noises and consequently make the task of radiologist to come up with the precise diagnosis. Although there are several denoising techniques for the same is available, while denoising they often suffer from the problem of eliminating the micron level details in the noise influenced images. It's a trade-off which prohibits efficient micro-classification of mammary tissues. Methods:In this study, we present a solution for the same by utilizing multi-level wavelet transformation to enable preservation of micron level details in the images. Results and Conclusion:The quality denoising without elimination of the features of the mammographic imagery data by MWTA will allow the medical practitioner to easily identify and consequently diagnose properly to cancer influenced patients.

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“…Many researchers relied on different ways, wavelet transform is mostly used because of its efficient properties especially the De-Correlation property of components of image with the high and low frequency content (Übeyl & Inan, 2004) (Agnew et al, 2011) (Florian & Thierry, 2007) (Akhilesh, 2012). Some researchers used discrete wavelet transform (DWT) (Fodor & Kamath, 2003) (Ferreira & Borges, 2003) in ultrasound images. The DWT is very efficient from a computational point of view, but it has main disadvantages that it is shift variant.…”

Section: Introductionmentioning

confidence: 99%

Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques

Hussein

Hussein²

2016

CIS

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Ultrasound has been used as a diagnostic modality for many intraocular diseases, due its safety, low cost, real time and wide availability. Unfortunately, ultrasound images suffer from speckle artifact that is tissue dependent. In this work, we will offer a method to reduce speckle noise and improve ultrasound image to raise the human diagnostic performance. This method combined undecimated wavelet transform with a wavelet coefficient mapping function: where UDWT used to eliminate the noise and a wavelet coefficient mapping function used to enhance the contrast of denoised images obtained from the first component. This methods can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. The proposed method is experimentally evaluated via 60 ultrasound images of eye. It is demonstrated that the proposed method can further improve the image quality of ocular ultrasound; the results reveal the effectiveness and superiority of the proposed method.

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Analysis of mammogram classification using a wavelet transform decomposition

Cited by 83 publications

References 4 publications

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Wavelet Transformation for Enhancing Mammographic Images

Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques

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