Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

  title={Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features},
  author={Xulei Qin and Guolan Lu and Ioannis Sechopoulos and Baowei Fei},
  booktitle={Medical Imaging},
Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT… 


  • J. Melo
  • Computer Science, Medicine
  • 2017
This work describes a CAD system to locate lesions in 2D mammographies, based on image processing and machine learning techniques, and proposes a two stage combination of classifiers to label mammograms.

Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

  • Siwa ChanJ. Yeh
  • Computer Science, Medicine
    Journal of Biotechnology Research
  • 2019
This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images and found that the LeNet-based CNN CAD significantly outperform the feature- based CAD.

Quantitative evaluation of fibroglandular tissue for estimation of tissue-differentiated absorbed energy in breast tomosynthesis. (Evaluation quantitative de tissu fibroglandulaire pour l'estimation de l'énergie absorbée différenciée par tissu en tomosynthèse du sein)

The objective quantification of the volumetric breast density was developed, based on already published methods, and improved and an improved quantity for the assessment of individual radiation-induced risk, in particular during mammography, was proposed together with a method to quantify it.

Evaluating attenuation correction strategies in a dedicated, single-gantry breast PET-tomosynthesis scanner

It is demonstrated that AC is necessary to obtain a closer estimate of the true lesion uptake and background activity in the breast, and <5% bias can be achieved by using a uniform patient-specific material to define the attenuation map.

X‐ray‐induced acoustic computed tomography for 3D breast imaging: A simulation study

The proposed breast XACT system has the ability to show the μCa cluster in 3D without any tissue superposition, and has great potential to be applied as a low-dose screening and diagnostic technique for early un-palpable lesion in the breast.

Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images

Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection and principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which are then used to register H SI images with the corresponding histological image.

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

The proposed improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, named as L0TDL outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning

Both numerical simulation and preclinical mouse experiments are performed to validate and evaluate the outperformances of proposedDL method by comparing with other state-of-the-art methods, such as total variation (TV) minimization and TV with low rank (TV + LR).

Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts

A registration method to map cardiac fiber orientations from three-dimensional (3D) magnetic resonance diffusion tensor imaging volume to the 2D ultrasound image to supply more detailed microstructure information of the myocardium is proposed.



Semi-automated segmentation and classification of digital breast tomosynthesis reconstructed images

  • S. VedanthamLinxi Shi K. Paulsen
  • Physics, Medicine
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2011
This work provides a segmentation and classification method to extract potential lesions, as well as adipose, fibroglandular, muscle and skin tissue in reconstructed DBT images that serve as anatomic priors during NIRS reconstruction.

Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study.

An algorithm for computerized detection of microcalcification clusters (MCCs) for DBT that operates on the projection views only, which does not depend on reconstruction, and is computationally efficient.

Computerized mass detection for digital breast tomosynthesis directly from the projection images.

The results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

Digital tomosynthesis in breast imaging.

Tomosynthesis may improve the specificity of mammography with improved lesion margin visibility and may improve early breast cancer detection, especially in women with radiographically dense breasts.

Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

The results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images and were able to correct the cupping artifacts and improve the quality of the Breast CT images.

Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment.

PURPOSE To correlate the parenchymal texture features at digital breast tomosynthesis (DBT) and digital mammography with breast percent density (PD), an established breast cancer risk factor, in a

Breast tomosynthesis: present considerations and future applications.

Although the technology has not yet been approved by the Food and Drug Administration, breast tomosynthesis has the potential to help reduce recall rates, improve the selection of patients for biopsy, and increase cancer detection rates, especially in patients with dense breasts.

Computerized Detection of Mass Lesions in Digital Breast Tomosynthesis Images Using Two- and Three Dimensional Radial Gradient Index Segmentation

Initial results for a computerized mass lesion detection scheme for digital breast tomosynthesis (DBT) images are presented and classification using 3D features was improved compared to the 2D equivalent features.