In this work, an automated system is designed to identify and classify the modality of medical images. We considered six modalities in this work: X-ray (XR), computed tomography (CT), magnetic resonance imaging (MR), positron emission tomography (PET), ultrasound (US) and photographs (PX). The methodology is based on encoding scale invariant feature transform (SIFT) features using Bag of Visual Words (BoVW), vector of locally aggregated descriptors (VLAD) and Fisher vector (FV). The encoded features are fed to support vector machine (SVM) classifier for training. The classification accuracy of all the classifiers based on three encoding strategies is compared and analyzed. The hybrid model is then implemented by selecting the best performance from each case. The major contribution of this research work is the application of VLAD for modality classification task which has not been tried so far. Combining the best performance of three encoding strategies, the overall classification accuracy obtained with the proposed system is 90.7%. For identification task, the scores from all the three encoding strategies are combined and the recognition rate obtained is 77.7%.

在这项工作中，设计了一个自动化系统来识别和分类医学图像的形式。我们在这项工作中考虑了六种模式：X射线（XR），计算机断层扫描（CT），磁共振成像（MR），正电子发射断层扫描（PET），超声（US）和照片（PX）。该方法基于使用视觉单词袋（BoVW），局部聚集描述符向量（VLAD）和Fisher向量（FV）的编码尺度不变特征变换（SIFT）特征。编码后的特征被馈送到支持向量机（SVM）分类器进行训练。比较和分析了基于三种编码策略的所有分类器的分类精度。然后，通过从每种情况中选择最佳性能来实施混合模型。这项研究工作的主要贡献是VLAD在模态分类任务中的应用，到目前为止尚未尝试过。结合三种编码策略的最佳性能，提出的系统获得的总体分类准确率为90.7％。对于识别任务，将所有三种编码策略的得分相结合，获得的识别率为77.7％。