In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective...In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.展开更多
This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. T...This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...展开更多
Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has...Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.展开更多
To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level ...To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.展开更多
Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal jun...Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal junctional failure (DJF) after ASD surgery with a lower instrumented vertebra (LIV) at L5. Overview of Literature: Spinopelvic fixation from the lower thoracic vertebra to the pelvis is the current gold standard treatment for ASD. However, the LIV at L5 is acceptable in some cases. Methods: Fifty-six patients who underwent corrective surgery for ASD with LIV at L5 were included. The upper instrumented vertebra (UIV) was T7 in one patient, T9 in 14, T10 in three, T11 in four, T12 in eight, L1 in 10, and L2 in 16. Regarding clinical parameters, age, sex, curve types of Scoliosis Research Society-Schwab classification, number of levels fused, follow-up period, hip bone mallow density, revision surgery rate, and radiographic measurements were compared between the T (UIV: T7 - 10) and TL (UIV: T11 - L2) groups. Results: The revision surgery rate was 19.6% overall. In the T and TL groups, it was 27.8%, and 15.8%, respectively (p = 0.305). The rate of DJF in the T group (33.3%) was significantly higher than in the TL group (5.3%). The rate of proximal junctional kyphosis in the T group (55.6%) was higher than in the TL group (28.9%), with no significant difference. The mean global alignment, sagittal vertical axis, and C7 plumb line-central sacral vertical line were not different between both groups. Conclusions: ASD surgery with LIV set at L5 and UIV set at the thoracic vertebrae (T7 - T10) has a risk of adjacent segment disease.展开更多
文摘In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.
基金National Natural Science Foundation of China (60802084)
文摘This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...
文摘Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.
文摘To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.
文摘Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal junctional failure (DJF) after ASD surgery with a lower instrumented vertebra (LIV) at L5. Overview of Literature: Spinopelvic fixation from the lower thoracic vertebra to the pelvis is the current gold standard treatment for ASD. However, the LIV at L5 is acceptable in some cases. Methods: Fifty-six patients who underwent corrective surgery for ASD with LIV at L5 were included. The upper instrumented vertebra (UIV) was T7 in one patient, T9 in 14, T10 in three, T11 in four, T12 in eight, L1 in 10, and L2 in 16. Regarding clinical parameters, age, sex, curve types of Scoliosis Research Society-Schwab classification, number of levels fused, follow-up period, hip bone mallow density, revision surgery rate, and radiographic measurements were compared between the T (UIV: T7 - 10) and TL (UIV: T11 - L2) groups. Results: The revision surgery rate was 19.6% overall. In the T and TL groups, it was 27.8%, and 15.8%, respectively (p = 0.305). The rate of DJF in the T group (33.3%) was significantly higher than in the TL group (5.3%). The rate of proximal junctional kyphosis in the T group (55.6%) was higher than in the TL group (28.9%), with no significant difference. The mean global alignment, sagittal vertical axis, and C7 plumb line-central sacral vertical line were not different between both groups. Conclusions: ASD surgery with LIV set at L5 and UIV set at the thoracic vertebrae (T7 - T10) has a risk of adjacent segment disease.
