Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

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Regular inspection of transmission lines is an essential work, which has been implemented by either labor intensive or very expensive approaches. 3D reconstruction could be an alternative solution to satisfy the need for accurate and low cost inspection. This paper exploits the use of an unmanned aerial vehicle (UAV) for outdoor data acquisition and conducts accuracy assessment tests to explore potential usage for offsite inspection of transmission lines. Firstly, an oblique photogrammetric system, integrating with a cheap double-camera imaging system, an onboard dual-frequency GNSS (Global Navigation Satellite System) receiver and a ground master GNSS station in fixed position, is designed to acquire images with ground resolutions better than 3 cm. Secondly, an image orientation method, considering oblique imaging geometry of the dual-camera system, is applied to detect enough tie-points to construct stable image connection in both along-track and across-track directions. To achieve the best geo-referencing accuracy and evaluate model measurement precision, signalized ground control points (GCPs) and model key points have been surveyed. Finally, accuracy assessment tests, including absolute orientation precision and relative model precision, have been conducted with different GCP configurations. Experiments show that images captured by the designed photogrammetric system contain enough information of power pylons from different viewpoints. Quantitative assessment demonstrates that, with fewer GCPs for image orientation, the absolute and relative accuracies of image orientation and model measurement are better than 0.3 and 0.2 m, respectively. For regular inspection of transmission lines, the proposed solution can to some extent be an alternative method with competitive accuracy, lower operational complexity and considerable gains in economic cost.

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