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Brain disease diagnosis using local binary pattern and steerable pyramid

机译：使用局部二进制模式和可转向金字塔的脑病诊断

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摘要

Brain diseases can cause invisible disorders, cognitive and behavioral changes. Their symptoms vary widely. In some cases, treatment can improve the symptoms while in other cases injuries become permanent. Many disorders are progressive. Therefore, early and accurate diagnosis of disorder is essential for improving disorder condition and patient’s quality of life. This paper presents the brain disease diagnosis system in which two feature extraction methods are compared. One of the feature extraction methods uses local binary pattern and steerable pyramid (SP) to decompose magnetic resonance (MR) brain images into subbands which are termed as LBPSP subbands. Another feature extraction method uses SP solely to decomposeMR images into SP subbands. Energies over LBPSP and SP subbands are calculated. The features are subjected to backpropagation neural network classifier. To prove the effectiveness of the proposed system,multi-class disease classification is carried out on four MR image datasets. Also, ‘one-vs-all’ binary classification is performed on one of the datasets. Energy features of LBPSP subbands achievemulti-class classification accuracies of 97.67%, 97.27%, 94.67% and 85.01% on datasets DS-200, DS-310, DS-255 and DS-612, respectively. The performance measures of ‘one-vs-all’ binary class classification prove the competency and efficiency of LBPSP subband features over the existing methods.The comparative results of two feature extraction methods indicate that the energy features of LBPSP subbands have more discriminating potential than energy features of SP subbands. Experimental results reveal that energy features of LBPSP subbands lead to the existing classification methods.
机译：脑病会导致隐形疾病，认知和行为变化。他们的症状差异很大。在某些情况下，治疗可以改善症状，而在其他情况下伤害变为永久性。许多障碍是渐进的。因此，早期和准确的诊断紊乱对于改善疾病状况和患者的生活质量至关重要。本文介绍了脑病诊断系统，其中比较了两个特征提取方法。其中一个特征提取方法使用局部二进制图案和可操纵的金字塔（SP）将磁共振（MR）脑图像分解为子带被称为LBPSP子带。另一个特征提取方法仅使用SP分解成SP子带。计算LBPSP和SP子带的能量。该特征受到反向化神经网络分类器的特征。为了证明所提出的系统的有效性，在四个MR图像数据集上进行多级疾病分类。此外，在其中一个数据集上执行“一vs-all”二进制分类。 LBPSP子带的能量特征ApieveMulti-阶级分类，分别在DASetS-200，DS-310，DS-255和DS-612上分别为97.67％，97.27％，94.67％和85.01％。 “一维所有”二进制类分类的性能测量措施证明了LBPSP子带特征的能力和效率。两个特征提取方法的比较结果表明，LBPSP子带的能量特征比能量更具差异差异SP子带的特征。实验结果表明，LBPSP子带的能量特征导致现有的分类方法。

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