Characterization of Mammary Gland Tissue Using Joint Estimators of Minkowski Functionals

Mattfeldt, Torsten; Meschenmoser, Daniel; Pantle, Ursa; Schmidt, Volker

doi:10.5566/ias.v26.p13-22

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…In 2D Euclidean space, morphology can be quantified by the three measures: area fraction, boundary length and the Euler number (Mattfeldt et al, 2007), where the Euler number describes the connectivity of objects detected within an image (Legland et al, 2007). In 3D Euclidean space, the Minkowski functionals are related (by dimensional scaling factors) to volume, surface area, IMC and ITC, which in turn is related to the Euler characteristic (χ) by a dimensional scale factor (Arns et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

“…Two-dimensional (2D) images can be described by three Minkowski functionals: area fraction, boundary length and the Euler number (Mattfeldt et al, 2007), and these have been used to characterise tissue samples and have been linked to pathology classification using 2D images of breast tissue. Minkowski functionals were used for images segmented into phases of stroma, epithelium and luminal space.…”

Section: Introductionmentioning

confidence: 99%

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Characterising the tumour morphological response to therapeutic intervention

Savage

Katz

Eberst

et al. 2012

Disease Models &Amp; Mechanisms

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SUMMARYIn cancer, morphological assessment of histological tissue samples is a fundamental part of both diagnosis and prognosis. Image analysis offers opportunities to support that assessment through quantitative metrics of morphology. Generally, morphometric analysis is carried out on two-dimensional tissue section data and so only represents a small fraction of any tumour. We present a novel application of three-dimensional (3D) morphometrics for 3D imaging data obtained from tumours grown in a culture model. Minkowski functionals, a set of measures that characterise geometry and topology in n-dimensional space, are used to quantify tumour topology in the absence of and in response to therapeutic intervention. These measures are used to stratify the morphological response of tumours to therapeutic intervention. Breast tumours are characterised by estrogen receptor (ER) status, human epidermal growth factor receptor (HER)2 status and tumour grade. Previously, we have shown that ER status is associated with tumour volume in response to tamoxifen treatment ex vivo. Here, HER2 status is found to predict the changes in morphology other than volume as a result of tamoxifen treatment ex vivo. Finally, we show the extent to which Minkowski functionals might be used to predict tumour grade. Minkowski functionals are generalisable to any 3D data set, including in vivo and cellular systems. This quantitative topological analysis can provide a valuable link among biomarkers, drug intervention and tumour morphology that is complementary to existing, non-morphological measures of tumour response to intervention and could ultimately inform patient treatment.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterising the tumour morphological response to therapeutic intervention

Savage

Katz

Eberst

et al. 2012

Disease Models &Amp; Mechanisms

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“…These three phases may be understood as random closed sets (RACS) with positive volume fraction (volume processes). Applying methods of spatial statistics to digitized images, or by simple manual counting methods, it is possible to characterize these three phases quantitatively in terms of area fraction A A , boundary length density L A and Euler number per unit tissue area χ A (see, e.g., Mattfeldt et al, 2007). The three aforementioned specific intrinsic volumes have a clear stereological interpretation, hence they can be used for the estimation of stereological model parameters:V…”

Section: Introductionmentioning

confidence: 99%

“…In previous investigations, it has been shown that the texture of mammary tissue, as seen at low magnification, may be characterized quantitatively in terms of stereology (Mattfeldt et al, 1993(Mattfeldt et al, , 1996(Mattfeldt et al, , 2007Mattfeldt, 2003). Basically, glandular tissue may be subdivided into three phases, namely the epithelial cells (the tumour cells), the lumina, and the stroma, which together account for 100% of the tumour tissue.…”

Section: Introductionmentioning

confidence: 99%

Testing Histological Images of Mammary Tissues on Compatibility With the Boolean Model of Random Sets

Mrkvička¹,

Mattfeldt²

2011

Image Anal Stereol

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Methods for testing the Boolean model assumption from binary images are briefly reviewed. Two hundred binary images of mammary cancer tissue and 200 images of mastopathic tissue were tested individually on the Boolean model assumption. In a previous paper, it had been found that a Monte Carlo method based on the approximation of the envelopes by a multi-normal distribution with the normalized intrinsic volume densities of parallel sets as a summary statistics had the highest power for this purpose. Hence, this method was used here as its first application to real biomedical data. It was found that mastopathic tissue deviates from the Boolean model significantly more strongly than mammary cancer tissue does.

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“…Correct reconstruction of locally defined characteristics is however crucial for unbiased computation of morphological image characteristics such as connectivity, boundary length or surface area. Quantitative information on these characteristics is a valuable source of information in a wide range of applications such as metrology (Neuschaefer-Rube et al, 2008), pathology (Mattfeldt et al, 2007), environmental health (Stoeger et al, 2006) or design of materials (Frost et al, 2006). We therefore suggest to directly incorporate measurements of morphological image characteristics into the FOMs used to evaluate tomographic reconstruction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Tomographic Reconstruction Algorithms by Morphological Image Characteristics

Lück¹,

Kupsch²,

Lange³

et al. 2011

Image Anal Stereol

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We suggest a procedure for quantitative quality control of tomographic reconstruction algorithms. Our task-oriented evaluation focuses on the correct reproduction of phase boundary length and has thus a clear implication for morphological image analysis of tomographic data. Indirectly the method monitors accurate reproduction of a variety of locally defined critical image features within tomograms such as interface positions and microstructures, debonding, cracks and pores. Tomographic errors of such local nature are neglected if only global integral characteristics such as mean squared deviation are considered for the evaluation of an algorithm. The significance of differences in reconstruction quality between algorithms is assessed using a sample of independent random scenes to be reconstructed. These are generated by a Boolean model and thus exhibit a substantial stochastic variability with respect to image morphology. It is demonstrated that phase boundaries in standard reconstructions by filtered backprojection exhibit substantial errors. In the setting of our simulations, these could be significantly reduced by the use of the innovative reconstruction algorithm DIRECTT.

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Characterization of Mammary Gland Tissue Using Joint Estimators of Minkowski Functionals

Cited by 12 publications

References 18 publications

Characterising the tumour morphological response to therapeutic intervention

Characterising the tumour morphological response to therapeutic intervention

Testing Histological Images of Mammary Tissues on Compatibility With the Boolean Model of Random Sets

Statistical Analysis of Tomographic Reconstruction Algorithms by Morphological Image Characteristics

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