Bone scintigraphy is widely used to diagnose bone diseases. Accurate hotspot segmentation is a critical task for tumor metastasis diagnosis. In this paper, we propose an interactive approach to detect and extract hots...
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ISBN:
(纸本)9781479999897
Bone scintigraphy is widely used to diagnose bone diseases. Accurate hotspot segmentation is a critical task for tumor metastasis diagnosis. In this paper, we propose an interactive approach to detect and extract hotspots in thoracic region based on a new multiple instance learning (MIL) method called EM-MILBoost. We convert the segmentation problem to a multiple instance learning task by constructing positive and negative bags according to the input bounding box. In order to be robust against noisy input, we train a region-level hotspot classifier with EM-MILBoost and develop several segmentation strategies based on it. The experimental results demonstrate that our method outperforms other methods and is robust against various noisy input.
Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in mo...
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ISBN:
(纸本)9781509009107
Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.
Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing ...
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ISBN:
(纸本)9781467397155
Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.
The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstru...
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ISBN:
(纸本)9781467397155
The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstructed density *** approach estimates spectral signal-to-noise ratio(SSNR) of 3D reconstructed map by computing the ratio of signal power to noise power in frequency *** power distributions of signal and noise are estimated from structure particle region and surrounding region segmented by applying a mask *** proposed protocol of calculating SSNR,which we term mask-SSNR(mSSNR),is independent of the reconstruction algorithms and can be used for density maps reconstructed with any reconstruction ***,the mSSNR neither needs to split the dataset into halves like the Fourier shell correlation(FSC) approach,nor any original images or intermediate data like other SSNR calculation methods in this *** mSSNR provides a direct calculation of SSNR based on its original definition,and is proven to be a better approach.
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The...
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ISBN:
(纸本)9781509006212
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.
This study proposes a motion cue based pedestrian detection method with two-trame-filtering (Tff) for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff pr...
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This study proposes a motion cue based pedestrian detection method with two-trame-filtering (Tff) for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient (HTffOG) feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.
Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several chal...
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- Non-local mean (NLM) algorithm has been implemented effectively in MRI denoising and is always limited by its computational complexity. To reduce the computational burden of NLM in 3D MRI dataset, in this paper, we ...
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A new pupil location methodis proposed in eye-gaze tracking system. Firstly, input images are enhanced in order to reduce the influence of illumination. Secondly, multiple candidate thresholds are obtained in terms of...
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