Evaluation and visual exploratory analysis of DCE-MRI Data of breast lesions based on morphological features and novel dimension reduction methods

Lespinats, Sylvain; Meyer‐Baese, Anke; Steinbrücker, Frank; Schlossbauer, Thomas

doi:10.1109/ijcnn.2009.5178627

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Furthermore, non-mass-enhancing lesions such as DCIS or ICS can be better differentiated based on morphological properties [25]. In a previous work [37], we have considered features that describe the geometric characteristics of the shape and local moments such as Krawtchouk to identify the non-smooth surface.…”

Section: Morphological Featuresmentioning

confidence: 99%

Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Hoffmann

Shutler

Lobbes

et al. 2013

EURASIP J. Adv. Signal Process.

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

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Section: Morphological Featuresmentioning

confidence: 99%

Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Hoffmann

Shutler

Lobbes

et al. 2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Furthermore, non-mass enhancing lesions such as DCIS or ICS can be better differentiated based on morphologic properties [20]. In the previous work [21], we have considered features that describe the geometric characteristics of the shape and local moments such as that of Krawtchouk to identify the non-smooth surface.…”

Section: Features Describing the Lesion Morphologymentioning

confidence: 99%

Computer-aided diagnosis for diagnostically challenging breast lesions in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics

Retter¹,

Plant

Burgeth³

et al. 2013

EURASIP J. Adv. Signal Process.

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Diagnostically challenging lesions comprise both foci (small lesions) and non-mass-like enhancing lesions and pose a challenge to current computer-aided diagnosis systems. Motion-based artifacts lead in dynamic contrast-enhanced breast magnetic resonance to diagnostic misinterpretation; therefore, motion compensation represents an important prerequisite to automatic lesion detection and diagnosis. In addition, the extraction of pertinent kinetic and morphologic features as lesion descriptors is an equally important task. In the present paper, we evaluate the performance of a computer-aided diagnosis system consisting of motion correction, lesion segmentation, and feature extraction and classification. We develop a new feature extractor, the radial Krawtchouk moment, which guarantees rotation invariance. Many novel feature extraction techniques are proposed and tested in conjunction with lesion detection. Our simulation results have shown that motion compensation combined with Minkowski functionals and Bayesian classifier can improve lesion detection and classification.

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“…Furthermore, non-mass enhancing lesions such as DCIS or ICS can be better differentiated based on morphological properties. 8 In previous work, 9 we have considered features that describe the geometric characteristics of the shape and local moments such as Krawtchouk to identify the non-smooth surface.…”

Section: Features Describing the Lesion Morphologymentioning

confidence: 99%

Improved computer-aided diagnosis for breast lesions detection in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics

Retter¹,

Plant

Burgeth³

et al. 2011

SPIE Proceedings

Self Cite

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Motion-based artifacts lead in breast MRI to diagnostic misinterpretation and therefore represents an important prerequisite to automatic lesion detection and diagnosis. In the present paper, we evaluate the performance of a computer-aided diagnosis (CAD) system consisting of motion correction, lesion segmentation, feature extraction and classification. Many novel feature extraction techniques are proposed and tested in conjunction with motion correction and classification. Our simulation results have shown that motion compensation combined with Minkowsi functionals and Bayesian classifier can improve the lesion detection and classification.

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Evaluation and visual exploratory analysis of DCE-MRI Data of breast lesions based on morphological features and novel dimension reduction methods

Cited by 4 publications

References 15 publications

Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Computer-aided diagnosis for diagnostically challenging breast lesions in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics

Improved computer-aided diagnosis for breast lesions detection in DCE-MRI based on image registration and integration of morphologic and dynamic characteristics

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