Fast Filtering Techniques in Medical Image Classification and Retrieval

This article presents the participation of the MIILab (Medical Image Information Laboratory) group in ImageCLEFmed2013. There are three types of tasks for ImageCLEFmed2013: modality classification, image retrieval and compound–image separation. Image modality classification and medical image retrieval are targeted according to MIILab’s research interest. The main goal is to perform a feasibility test on applying existing techniques on new applications, such as applying image denoising techniques on image retrieval and classification. Both global features and local features were employed. Fast filtering techniques were used to obtain global features on color, shape and texture. These global features serves to perform a pre–classification on images. Both low–level and high–level local features were extracted. Bags of features model was used to build final feature vector. Both kNN and SVM classifiers were tried out in modality classification task. Reciprocal kNN was used to perform result fusion in image retrieval task. The modality classification task was decomposed into a compound image classification and a non–compound image classification. Our approaches achieved 89.9% classification accuracy on training data and 85.1% on testing data for compound image classification. For non compound image classification, accuracy is around 68.3% on training data and 67.7% on testing data. The overall classification accuracy is around 65% on 31 classes. False alarms are mainly from large classes such as compound images (312), X–ray images (101) and organ photos (63), but accuracy per class shows that performance bottleneck also comes from small/medium classes with large content diversity such as statistic figures, chemical structure and 3D images. Best result was around 80% (IBM research lab). For the image retrieval task, one baseline using SURFContext+BoF was submitted and the corresponding MAP (mean average precision) is 0.0086. Even best visual retrieval run obtained only a MAP of 0.018, which is still not comparable with textual approaches (average score 0.2).