Comparison and Assessment of Various Wavelet and Wavelet Packet based Denoising Algorithms for Noisy Data

Hess, F; Kraft, Markus; Richter, M.; Bockhorn, Henning

doi:10.1007/978-3-322-96688-9_26

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…8(a), it can be seen that increases monotonically as increases. This can be expected in (19). From Fig.…”

Section: Performance Of the Methods Under Gaussian Distributionssupporting

confidence: 66%

“…The image in scale 2 is used for segmentation. For the second step, Daubechies 6-point wavelet [11] is used again for analysis of PDF of the wavelet transformed image because of its performance in denoising [19]. Next, we take 5-scale wavelet transforms for the histogram of the image in scale 2.…”

Section: Examplesmentioning

confidence: 99%

“…In this scale, it can be expected that most noise and singularities can be suppressed by dropping the coefficients of the wavelet channels, and the bright target-tumor information remains in the scaling channel. In PDF analysis, we still selected Daubechies 6-point wavelet because of its good performance in noise removal [19]. We not only want to remove the noise from the PDF curve, but also want to remove the fluctuation within the curve.…”

Section: Examplesmentioning

confidence: 99%

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Segmentation of bright targets using wavelets and adaptive thresholding

Zhang

Desai

2001

IEEE Trans. on Image Process.

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Abstract-A general systematic method for the detection and segmentation of bright targets is developed in this paper. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.

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“…8(a), it can be seen that increases monotonically as increases. This can be expected in (19). From Fig.…”

Section: Performance Of the Methods Under Gaussian Distributionssupporting

confidence: 66%

Section: Examplesmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

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Segmentation of bright targets using wavelets and adaptive thresholding

Zhang

Desai

2001

IEEE Trans. on Image Process.

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“…For the processing of PDF curves to generate the global threshold, we apply one-dimensional UWT using Daubechies 6-point (Db 6) wavelet. These are reported to be fairly good for signal denoising processes [15].…”

Section: B Global Thresholding and Morphological Enhancementmentioning

confidence: 79%

Suspicious lesion detection in mammograms using undecimated wavelet transform and adaptive thresholding

Nayak

Ghosh

Ari

2013

2013 15th International Conference on Advanced Computing Technologies (ICACT)

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Mammographic screening is the most effective procedure for the early detection of breast cancers. However, typical diagnostic signs such as masses are difficult to detect as mammograms are low-contrast noisy images. This paper proposes a systematic method for the detection of suspicious lesions in digital mammograms based on undecimated wavelet transform and adaptive thresholding techniques. Undecimated wavelet transform is used here to generate a multiresolution representation of the original mammogram. Adaptive global and local thresholding techniques are then applied to segment possible malignancies. The segmented regions are enhanced by using morphological filtering and seeded region growing. The proposed method is evaluated on 120 images of the Mammographic Image Analysis Society (MIAS) Mini Mammographic database, that include 89 images having in total 92 lesions. The experimental results show that the proposed method successfully detects 87 of the 92 lesions, performing with a sensitivity of 94.56% at 0.8 false positives per image (FPI), which is better than earlier reported techniques. This shows the effectiveness of the proposed system in detecting breast cancer in early stages.

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