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Objective To analyze the statistical behavior of plantar pressure distribution, extract the characteristics of foot movement, and provide references for application of gait recognition in medical clinical diagnosis, rehabilitation training and public health. Methods The collected foot pressure data were prepossessed, statistical analysis on the data was performed, the footprint reconstruction was realized, and the pressure distribution rates of the footprints, segmented regions and each region were compared and analyzed, so as to decompose the foot motion characteristics. Results Based on the cross point of pressure peak curve in different regions, the plantar region was divided into toe region, metatarsal region, arch region and heel region, which could accurately extract the foot movement characteristics. Conclusions The peak plantar pressure is used to extract the characteristics of foot movement, which is divided into landing stage, whole foot contact stage, heel tiptoe stage and ground off stage.
RESUMO
Objective:To evaluate the feasibility of magnetic resonance (MR) perfusion imaging for sub-region segmentation of brain metastases (BMs), and to provide reference for individualized radiotherapy based on blood flow perfusion heterogeneity in BMs patients.Methods:96 BMs patients were selected, including 55 patients with necrosis and 41 without necrosis. Each patient was scanned with CT simulation and MR simulation before radiotherapy. MIM Maestro 6.8.8 software was used to delineate the gross tumor volume (GTV) and necrosis GTV (GTV N) from enhanced T 1W images and T 2 Propeller images, respectively, and the solid GTV (GTV S) was obtained by the subtraction of the two. Then, the cerebral blood flow map of three dimensional arterial spin labeling (3D-ASL) was employed to determine the high perfused GTV (GTV H) and low perfused GTV (GTV L). The volume and proportion of sub-regions were counted and compared between two groups and the correlation of each sub-region was analyzed. Results:The volume of GTV in the necrosis and non-necrosis groups was 19.56 and 7.34 cm 3, respectively. Besides, the AUC of the ROC between GTV volume and necrosis was 0.749. In the necrosis group, the ratio of GTV N, GTV S, GTV H and GTV L to GTV was 20.47%, 79.53%, 33.03% and 46.50%, respectively (all P<0.05). Among them, the r value between GTV S and GTV was 0.963, 0.849 for GTV L and GTV, and 0.840 for GTV L and GTV S, significantly higher than 0.683 for GTV H and GTV and 0.764 for GTV H and GTV S (all P<0.05). In the non-necrosis group, the ratio of GTV H to GTV was higher than that in the necrosis group (58.95% vs. 33.03%, P<0.05). In addition, the ratio of GTV L to GTV was slightly lower than that in the necrosis group (41.05% vs. 46.50%, P>0.05). The r value between GTV H and GTV was 0.776, significantly higher than 0.574 between GTV L and GTV ( P<0.05). Conclusion:MR-3D-ASL can quantitatively analyze the heterogeneous blood perfusion of BMs, which could guide the sub-region segmentation and local dose escalation of tumors.
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Optical coherence tomography (OCT) has become a key technique in the diagnosis of coronary artery stenosis, which can identify plaques and vulnerable plaques in the image. Therefore, this technique is of great significance for the diagnosis of coronary heart disease. However, there is still a lack of automatic, multi-region, high-precision segmentation algorithms for coronary OCT images in the current research field. Therefore, this paper proposes a multi-zone, fully automated segmentation algorithm for coronary OCT images based on neutrosophic theory, which achieves high-precision segmentation of fibrous plaques and lipid regions. In this paper, the method of transforming OCT images into T in the area of neutrosophics is redefined based on the membership function, and the segmentation accuracy of fiber plaques is improved. For the segmentation of lipid regions, the algorithm adds homomorphic filter enhancement images, and uses OCT to transform OCT images into I in the field of neutrosophics, and further uses morphological methods to achieve high-precision segmentation. In this paper, 40 OCT images from 9 patients with typical plaques were analyzed and compared with the results of manual segmentation by doctors. Experiments show that the proposed algorithm avoids the over-segmentation and under-segmentation problems of the traditional neutrosophic theory method, and accurately segment the patch area. Therefore, the work of this paper can effectively improve the accuracy of segmentation of plaque for doctors, and assist clinicians in the diagnosis and treatment of coronary heart disease.