Rampun Andrik, Morrow Philip J, Scotney Bryan W, Wang Hui
Academic Unit of Radiology, Department of Infection, Immunity and Cardiovascular Disease, Sheffield University, S10 2RX, UK; School of Computing, Ulster University, Jordanstown, Northern Ireland, BT37 0QB, UK.
School of Computing, Ulster University, Jordanstown, Northern Ireland, BT37 0QB, UK.
Comput Biol Med. 2020 Jul;122:103842. doi: 10.1016/j.compbiomed.2020.103842. Epub 2020 Jun 3.
We investigate various channel encoding techniques applied to breast density classification in mammograms; specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.
我们研究了应用于乳房X光片中乳腺密度分类的各种信道编码技术；具体而言，考虑了局部二进制、三进制和五进制编码方法。随后，我们提出了一种基于七编码技术的新编码方法，产生了一种名为局部七进制模式算子的新局部模式算子。实验结果表明，在乳房X光图像分析协会（MIAS）和InBreast数据集上进行评估时，所提出的局部模式算子具有鲁棒性，并且优于其他编码技术。局部七进制模式算子在MIAS和InBreast数据集上分别达到了83.3%和80.5%的最大分类准确率。其他局部模式算子最接近的比较结果是局部五进制算子，其在MIAS和InBreast数据集上的最大准确率分别为82.1%和80.1%。
