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Radiomics is a quantitative method to extract medical features that can extend to details that are invisible to human eyes. Studies have suggested that radiomics of the hippocampus is a potential imaging biomarker for several diseases, including Alzheimer's disease, mild cognitive impairment, and multiple sclerosis. This study investigates the test-retest reproducibility of radiomics features of the hippocampus. For this purpose, the intra-class correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) of the radiomics features of the hippocampus were well studied in four datasets consisting of 15, 19, 23, and 3 subjects who were scanned three, two, four, and forty times, respectively. The results highlight that the hippocampal texture features are the most repeatable, and that texture features are a possible neuroimaging biomarker for clinical studies.INDEX TERMS Hippocampus, radiomics features, repeatability.

While performing well in se gmenting on images with low SNR and low contrast, the three-Dimensional Otsu's Threshold Algorithm has the disadvantage of high computational complexity. By removing the redundant computation and computing look-up table with iteration, this paper improves the complexity greatly and gives a fast algorithm for the three-Dimensional Otsu's Threshold Algorithm. Experimental results show that the algorithm reduces calculation time substantially with no quality loss in the threshold compared with the original algorithm.

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In order to realize the recognition and localization of the target fruit more accurately and efficiently, an optimized graph-based recognition algorithm based on image depth information is proposed. Firstly, the gradient information of the depth image is obtained from the acquired depth apple image, the gradient vector is reflected from the 3D space to the 2D space, and then rotated 90 degrees clockwise, and the regular region forms the center of the vortex, which is the target fruit center to determine target fruit location. In the RGB space, the graph-based segmentation algorithm is introduced. By constructing the adaptive threshold, the mapping relationship between the number of centers and the parameters of k and min is established to find the target fruit region. Finally, scan the maximum radius of the super pixel area where the center of the circle is located, and fit the target fruit to realize the recognition and localization of the apple. The experimental results show that the target recognition rate is 96.61%, and the recognition time has been reduced significantly.