Spatial Distribution Analysis of Novel Texture Feature Descriptors for Accurate Breast Density Classification.

ABSTRACT: Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. In conventional processing schemes, image features are obtained by computing histograms from texture patterns. However, such processes ignore very important spatial information related to the texture features. This study designs a new feature vector, namely, K-spectrum, by using Baddeley's K-inhom function to characterise the

SUBMITTER: Li H 

PROVIDER: S-EPMC9002800 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature