Analyzing the Texture of Nakagami Parametric Images for Classification of Breast Cancer

Muhtadi, Sabiq; Chowdhury, Abdur; Razzaque, Rezwana R.; Shafiullah, Ahmad

doi:10.1109/nbec53282.2021.9618762

Cited by 1 publication

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“…32 Thus, we hypothesize that Nakagami parametric images should be able to provide a good indication of texture, and in this paper we present a comprehensive study on texture quantification of Nakagami parametric images for breast tumor characterization. We build on our earlier work which explored texture quantification of Nakagami images in a smaller capacity, 37 with promising results.…”

Section: Introductionmentioning

confidence: 99%

Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%