@article{muhtadi_razzaque_chowdhury_garra_kaisar alam_2023, title={Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization}, volume={10}, ISSN={["2329-4310"]}, DOI={10.1117/1.JMI.10.S2.S22410}, abstractNote={Abstract. Purpose We evaluate texture quantified from ultrasound Nakagami parametric images for non-invasive characterization of breast tumors, as Nakagami images can more faithfully represent intrinsic tumor characteristics than standard B-mode images. Approach Parametric images were formed using sliding windows applied to ultrasound envelope data. To analyze the trade-off between spatial resolution and stability of estimated Nakagami parameters for texture quantification, two different window sizes were used for image formation: (i) the standard square window with sides equal to three times the pulse length of incident ultrasound, and (ii) a smaller square window with sides equal to exactly the pulse length. Texture was quantified from two different regions of interest (ROIs) consisting of the tumor core and a 5 mm surrounding margin. A total of 186 texture features were analyzed for each ROI, and feature selection was used to identify the most relevant feature sets for breast tumor characterization. Results Texture quantified from parametric images formed using the two different windows did not outperform each other by a significant margin. However, when the mean pixel value within the tumor region of the parametric images was incorporated with the texture features, texture quantified from the tumor core and surrounding margin of images formed using the standard square window thoroughly outperformed other considerations for breast lesion characterization. The highest performing set of texture and mean value features yielded a significant AUC of 0.94, along with sensitivity of 90.38% and specificity of 89.58%. Conclusions We establish that texture quantified from ultrasound Nakagami parametric images are diagnostically relevant and may be used to characterize breast lesions effectively.}, journal={JOURNAL OF MEDICAL IMAGING}, author={Muhtadi, Sabiq and Razzaque, Rezwana R. and Chowdhury, Ahmad and Garra, Brian S. and Kaisar Alam, S.}, year={2023}, month={Feb} }
 @article{muhtadi_haque_gallippi_2022, title={Combined B-mode and Nakagami Images for Improved Discrimination of Breast Masses using Deep Learning}, ISSN={["1948-5719"]}, DOI={10.1109/IUS54386.2022.9957624}, abstractNote={Although ultrasound has become an important screening tool for the non-invasive diagnosis of breast cancer, it is limited by intra- and inter-observer variability, and subjectivity in diagnosis. On the other hand, deep learning-based approaches have the potential for objective and automated diagnosis in a manner that is efficient and reproducible. In this study, we propose a deep learning methodology for the classification of benign and malignant breast lesions based on combined ultrasound B-mode and Nakagami images. We hypothesize that combining the images, which contain complementary information, will provide better classification performance in a deep learning framework than using the images by themselves. The study included 230 patients who had 152 benign and 78 malignant masses. Nakagami images were formed using a sliding window applied to the envelope data of each patient. A superposition approach was adopted to form fused images, where Nakagami images and B-mode images were superimposed onto each other at differing weights. A modified VGG-16 network was trained on the resulting images, and performance was evaluated on a separate test dataset containing 50 images. Models trained using fused images outperformed models trained using individual B-mode and Nakagami images. Furthermore, the AVCs obtained by models trained on fused images were found to be statistically significantly higher than models trained on individual images. The obtained results demonstrate the feasibility of combining information from Nakagami and B-mode images, and its potential to provide improved diagnosis for breast cancer.}, journal={2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)}, author={Muhtadi, Sabiq and Haque, Syed Tousiful and Gallippi, Caterina M.}, year={2022} }