Morphological features for breast mass characterization (in construction)

This Matlab repository presents how to extract morphological features based on manually outlined regions of interests (ROIs) from the OASBUD dataset.

List of morphological features:

• breast mass area
• area ratio
• circularity
• convexity
• depth-to-width ratio
• elliptic normalized circumference
• elliptic normalized skeleton
• long-to-short axis ratio
• mass orientation
• normalized residual value
• roundness
• normalized radial length area ratio
• normalized radial length contour roughness
• normalized radial length mean
• normalized radial length standard deviation

The listed features were investigated in the following papers:

• Chen et al. Breast lesions on sonograms: computer-aideddiagnosis with nearly setting-independent features and artificial neuralnetworks. Radiology, 2003.
• Shen et al. Breast ultrasound computer-aided diagnosis using BI-RADS features. Academic Radiology, 2007.
• Alvarenga et al. Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images. Medical Engineering & Physics, 2010,
• Flores et al. Improving classification performance of breast lesions on ultrasonography. Pattern Recognition, 2015.

About

If you find this site useful for your work, consider citing the following papers:

• Byra et al. Early prediction of response to neoadjuvant chemotherapy in breastcancer sonography using Siamese convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 2020.

• Piotrzkowska-Wroblewska et al. Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions. Medical Physics, 2017.

e-mail: [email protected]

Acknowledgement

We used the publically available Matlab code for ellipse fitting:

Ohad Gal (2020). fit_ellipse (https://www.mathworks.com/matlabcentral/fileexchange/3215-fit_ellipse), MATLAB Central File Exchange.