Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images

Marco Caballo; Jonas Teuwen; Ritse Mann; Ioannis Sechopoulos

doi:10.1117/12.2511718

13 March 2019 Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images

Marco Caballo, Jonas Teuwen, Ritse Mann, Ioannis Sechopoulos

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500J (2019) https://doi.org/10.1117/12.2511718
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We propose an algorithm to recognize breast parenchyma regions containing mass-like abnormalities in dedicated breast CT images using texture feature descriptors. From 53 patient breast CT scans (29 of which containing masses), we first isolated the parenchyma through automatic segmentation, and we obtained a total of 14,751 normal 2D image patches (negatives), and 2,100 containing a breast mass (positives). We extracted 141 texture features (10 first-order descriptors, 6 Haralick features, 20 run-length features, 45 structural and pattern descriptors, 60 Gabor features), which we then analyzed through multivariate analysis of variance (MANOVA) and linear discriminant analysis, resulting in an area under the ROC curve (AUC) of 0.9. We finally identified the most discriminant features through sequential forward selection, and used them to train and validate a neural network by dividing the data into multiple batches, with each batch always containing the whole set of positive cases, and as many different negative examples. To avoid the possible bias due to the high skewness in class proportion, the training was performed on all these batches independently, without re-initializing the network weights after each training. The network was tested using an additional independent 18 patient breast CT scans (8 normal and 10 containing a mass), on a total of 7,274 image patches (852 positives, 6,422 negatives) which were not used during the training/validation phase, resulting in 95.6% precision, 95.8% recall, and 0.99 AUC. Our results suggest that the proposed approach could be further evaluated and expanded for computer-aided detection tasks in breast CT imaging.

Citation Download Citation

Marco Caballo, Jonas Teuwen, Ritse Mann, and Ioannis Sechopoulos "Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500J (13 March 2019); https://doi.org/10.1117/12.2511718

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available