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

Sensors

Shelley Boyd¹  Ramakrishnan Mukundan²  Haipeng Li² 

[1] Canterbury Breastcare, St. George’s Medical Centre, Christchurch 8014, New Zealand;Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8140, New Zealand;

关键词: breast density classification;    mammography;    local quinary patterns;    spatial distribution analysis;    texture features;   

DOI  :  10.3390/s22072672

来源: DOAJ

【 摘 要 】

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 spatial distribution information of feature point sets. Texture features extracted by RIU4-LQP and K-spectrum are utilised to classify mammograms into BI-RADS density categories. Three feature selection methods are employed to optimise the feature set. In our experiment, two mammogram datasets, INbreast and MIAS, are used to test the proposed methods, and comparative analyses and statistical tests between different schemes are conducted. Experimental results show that our proposed method outperforms other approaches described in the literature, with the best classification accuracy of 92.76% (INbreast) and 86.96% (MIAS).

【 授权许可】

Unknown