Structural magnetic resonance imaging (sMRI)-based Alzheimer’s disease (AD) classification and its prodromal stage–mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git .

The proliferation of IoT devices generates vast amounts of sensor data, making sensor data mining an increasingly important research topic in the IoT domain. The Top-k similarity search plays a crucial role in sensor data mining, forming the foundation for advanced techniques such as sensor data classification, clustering, and anomaly detection. The need for efficient algorithms to enhance search speed drives our research. This paper introduces an efficient method named PIPDTW for Top-k similarity search of sensor data using Dynamic Time Warping (DTW) distance, a prevalent but computationally intensive similarity measure in time-series data mining. To address the high computational complexity, PIPDTW first employs perceptually important points (PIPs) to transform lengthy sensor data into concise vector representations. We then introduce a novel distance measure, DTWIV, based on PIP representations to quickly estimate DTW distances between the query q and all sensor data. Finally, during the online search, we apply two levels of filtering based on approximate ranking and the lower-bound technique to efficiently prune dissimilar data, reducing the need for exact DTW distance computations and thereby achieving efficient querying. Experiments conducted on synthetic and real-world publicly available datasets, compared with relevant competitors, demonstrate that PIPDTW slightly sacrifices retrieval accuracy but significantly reduces runtime.