Distinguishing Cancer and Normal Breast Tissue Autofluorescence Using Continuous Wavelet Transform

Gharekhan, Anita H.; Arora, Siddharth; Panigrahi, Prasanta K.; Pradhan, Asima

doi:10.1109/jstqe.2009.2033018

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…Using CWT, the vital signs phase signal disturbed by the artifacts and its corresponding CWT can be extracted. The artifacts can be identified in the time domain exploiting the frequency information [33] - [36].…”

Section: Background On Cwtmentioning

confidence: 99%

Optimized Continuous Wavelet Transform Algorithm Architecture and Implementation on FPGA for Motion Artifact Rejection in Radar-Based Vital Signs Monitoring

et al. 2022

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The continuous wavelet transform (CWT) has been used in radar-based vital signs detection to identify and to remove the motion artifacts from the received radar signals. Since the CWT algorithm is computationally heavy, the processing of this algorithm typically results in long processing time and complex hardware implementation. The algorithm in its standard form typically uses software processing tools and is unable to support high-performance data processing. The aim of this research is to design an optimized CWT algorithm architecture to implement it on Field Programmable Gate Array (FPGA) in order to identify the unwanted movement introduced in the retrieved vital signs signals. The optimization approaches in the new implementation structure are based on utilizing the frequency domain processing, optimizing the required number of operations and implementing parallel processing of independent operations. Our design achieves significant processing speed and logic utilization optimization. It is found that processing the algorithm using our proposed hardware architecture is 48 times faster than processing it using MATLAB. It also achieves an improvement of 58% in speed performance compared to alternative solutions reported in literature. Moreover, efficient resources utilization is achieved and reported. This advanced performance of the proposed design is due to consciously implementing comprehensive approaches of multiple optimization techniques that results in multidimensional improvements. As a result, our achieved design is suitable for utilization in high-performance data processing applications.INDEX TERMS Continuous wavelet transform, FPGA implementation, radar remote sensing, motion artifact rejection, random body movements, FFT-based CWT, parallel processing.

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Section: Background On Cwtmentioning

confidence: 99%

Optimized Continuous Wavelet Transform Algorithm Architecture and Implementation on FPGA for Motion Artifact Rejection in Radar-Based Vital Signs Monitoring

et al. 2022

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“…1 Optical tools are sensitive and are hence potentially capable of discriminating different stages of disease progression. [1][2][3][4][5][6][7] However, tissue being a complex medium, with several fluorophores, scatterers, and absorption domains, makes it difficult for proper diagnosis through optical means. 1 Hence, identifying reliable markers for accurately depicting the tissue condition through noninvasive optical methods has received significant attention.…”

Section: Tissue Multifractality and Hidden Markov Model Based Integramentioning

confidence: 99%

“…1 For this purpose, recent approaches have focused on extracting intrinsic fluorescence 2 and tissue multifractality, characterizing the morphological changes by multifractal detrended fluctuation analysis (MFDFA). 3 Other approaches make use of principal component analysis 4 for identification of underlying spectral correlation and other image processing tools like wavelets 5 for pin pointing parameters that faithfully capture the disease progression. Clinical application of this approach, not only depends on these biomarkers but also crucially depends on the validation of the diagnosis outcome through a suitable diagnostic algorithm, which can accurately classify the measured spectra from an unknown tissue, using the stored database of spectra of tissues of known histopathologic classification.…”

Section: Tissue Multifractality and Hidden Markov Model Based Integramentioning

confidence: 99%

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Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

et al. 2017

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We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

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“…Wavelet transform due to it's multi-resolution analysis capability using the Daubechies' basis which extract the polynomial trends (for example, Db-4 and Db-6 extract the linear and quadratic trends respectively) has been shown to characterize the scaling behavior and selfsimilarity of empirical data sets quite faithfully [38,39]. Indeed, it has been initially explored to analyze tissue fluorescence spectra in an attempt to distinguish between normal and dysplastic tissue [40,41,42,43,44]. In this work, we employ this multi-resolution property of wavelets to ascertain the changes in the self-similarity of dysplastic human cervical tissues as opposed to healthy human cervical tissues by analyzing the esoteric nature of the fluctuations in tissue light scattering spectra.…”

Section: Introductionmentioning

confidence: 99%

Differing self-similarity in light scattering spectra: A potential tool for pre-cancer detection

Ghosh,

Soni,

Purwar

et al. 2011

Preprint

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The fluctuations in the elastic light scattering spectra of normal and dysplastic human cervical tissues analyzed through wavelet transform based techniques reveal clear signatures of self-similar behavior in the spectral fluctuations. Significant differences in the power law behavior ascertained through the scaling exponent was observed in these tissues. The strong dependence of the elastic light scattering on the size distribution of the scatterers manifests in the angular variation of the scaling exponent. Interestingly, the spectral fluctuations in both these tissues showed multifractality (non-stationarity in fluctuations), the degree of multi-fractality being marginally higher in the case of dysplastic tissues. These findings using the multi-resolution analysis capability of the discrete wavelet transform can contribute to the recent surge in the exploration for non-invasive optical tools for pre-cancer detection.

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Distinguishing Cancer and Normal Breast Tissue Autofluorescence Using Continuous Wavelet Transform

Cited by 13 publications

References 17 publications

Optimized Continuous Wavelet Transform Algorithm Architecture and Implementation on FPGA for Motion Artifact Rejection in Radar-Based Vital Signs Monitoring

Optimized Continuous Wavelet Transform Algorithm Architecture and Implementation on FPGA for Motion Artifact Rejection in Radar-Based Vital Signs Monitoring

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

Differing self-similarity in light scattering spectra: A potential tool for pre-cancer detection

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