Diagnostic Classification of Digital Mammograms by Wavelet-Based Spectral Tools: A Comparative Study

Hamilton, Erin K.; Jeon, Seonghye; Cobo, Pepa Ramirez; Lee, Kichun Sky; Vidaković, Brani

doi:10.1109/bibm.2011.44

Cited by 15 publications

(14 citation statements)

References 10 publications

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“…This method weights each level by the inverse of the variance of that level. Hamilton et al (2011) proposed estimation methods that are based on the Theil regression, that is, a weighted average of all pairwise slopes s ij between levels i and j. Given a weight w ij , the estimator of the overall slope in (16) is then i,j w ij s ij / i,j w ij .…”

Section: Discrete Complex Waveletsmentioning

confidence: 99%

“…The robust estimation approaches include Abry-Veitch weighted regression (AV), level enhanced OLS (EOLS) and harmonic average weighted slopes (HA). For more details on these robust estimators, we refer the readers to Veitch and Abry (1999); Hamilton et al (2011). Note that the wavelet spectra slope is used as a predictor instead of the Hurst exponent.…”

Section: Mammogram Classificationmentioning

confidence: 99%

“…Most of the references found in literature dealing with breast cancer detection methods are based on microcalcifications (Wang and Karayiannis, 1998;Netsch and Peitgen, 1999;Kestener et al, 2001;El-Naqa et al, 2002). Only recently the information contained in the background is taken into consideration (Nicolis et al, 2011;Hamilton et al, 2011). This classifying measure based on the irregularity measure of background tissue promises to be a new tool to be used in combination with existing clinical diagnostic tools, thus improving discriminatory power of non-invasive diagnostic techniques.…”

Section: Introductionmentioning

confidence: 99%

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Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Jeon

Nicolis

Vidaković

2014

São Paulo J. Math. Sci.

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Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most eective method for detecting breast cancer early; however, radiological inter- pretation of mammogram images is a challenging task. Many medical images demonstrate a certain degree of self-similarity over a range of scales. This scaling can help us to describe and classify mammograms. In this work, we generalize the scale-mixing wavelet spectra to the complex wavelet domain. In this domain, we estimate Hurst parameter and image phase and use them as discriminatory descriptors to clas- sify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM). Keywords: Scaling; Complex Wavelets; Self-similarity; 2-D Wavelet Scale-Mixing Spectra.

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Section: Discrete Complex Waveletsmentioning

confidence: 99%

Section: Mammogram Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Jeon

Nicolis

Vidaković

2014

São Paulo J. Math. Sci.

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“…These measures can be used in the analysis of signals and images as informative summaries. In medical applications, fractal dimension and Hurst exponent are widely used for the analysis, characterization, and classification of mammogram images .…”

Section: Mammary Cancer Classification Using Complex Wavelet‐based Sementioning

confidence: 99%

Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

et al. 2015

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Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.

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“…In the literature, several approaches were proposed to segment microcalcifications [15][16][17][18] such as active contours [16,19], curvelet moments [20], wavelet analysis [21][22][23], fractal analysis [24][25][26], multifractal analysis [27,28] and morphological filters [29][30][31][32] in order to reduce human subjectivity in diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

Sahli¹

2018

IJMI

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This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: 'Sq-Sq' approach based on multifractal analysis and the 'MM' approach based on Mathematical Morphology. In fact, the results proved that the 'Sq-Sq' method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The 'Sq-Sq' approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.

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Diagnostic Classification of Digital Mammograms by Wavelet-Based Spectral Tools: A Comparative Study

Cited by 15 publications

References 10 publications

Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

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