This invention provides a signal-processing method that makes it possible to acquire, relatively easily and surely, a highly reliable normalized impulse-response signal without relying on the signal-correction processing after normalization. The signal-processing method of this invention includes a low-frequency extraction step, a high-frequency extraction step and a synthesizing step. In the low-frequency extraction step, only the low-frequency component is extracted from the spectrum of the first normalized signal NS1 obtained by normalizing the target signal S.sub.tgt in the time domain. In the high-frequency extraction step, only the high-frequency component is extracted from the spectrum of the second normalized signal NS2 obtained by normalizing the target signal S.sub.tgt in the frequency domain using the reference signal S.sub.ref. In the synthesizing step, the low-frequency component, derived from the first normalized signal NS1, and the high-frequency component, derived from the second normalized signal NS2, are synthesized to obtain a normalized impulse-response signal NS.

This invention provides a signal-processing method that makes it possible to acquire, relatively easily and surely, a highly reliable normalized impulse-response signal without relying on the signal-correction processing after normalization. The signal-processing method of this invention includes a low-frequency extraction step, a high-frequency extraction step and a synthesizing step. In the low-frequency extraction step, only the low-frequency component is extracted from the spectrum of the first normalized signal NS1 obtained by normalizing the target signal S.sub.tgt in the time domain. In the high-frequency extraction step, only the high-frequency component is extracted from the spectrum of the second normalized signal NS2 obtained by normalizing the target signal S.sub.tgt in the frequency domain using the reference signal S.sub.ref. In the synthesizing step, the low-frequency component, derived from the first normalized signal NS1, and the high-frequency component, derived from the second normalized signal NS2, are synthesized to obtain a normalized impulse-response signal NS.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

Systems and methods are provided in which intraoperatively acquired surface data is employed to verify the correspondence of an intraoperatively selected spinal level with a spinal level that is pre-selected based on volumetric image data. Segmented surface data corresponding to the pre-selected spinal levels may be obtained from the volumetric image data, such that the segmented surface data corresponds to a spinal segment that is expected to be exposed and identified intraoperatively during the surgical procedure. The segmented surface data from the pre-selected spinal level, and adjacent segmented surface data from an adjacent spinal level that is adjacent to the pre-selected spinal level, is registered to the intraoperative surface data, and quality measures associated with the registration are obtained, thereby permitting an assessment or a determination of whether or not the pre-selected spinal surface (in the volumetric frame or reference) is likely to correspond to the intraoperatively selected spinal level.

In a MR water-fat image separation method and device, within one echo period, a first echo set under a first readout gradient polarity and a second echo set under a second readout gradient polarity are acquired. The first and second readout gradient polarities may be opposite, and echoes in the first echo set may be positionally one-to-one symmetric to echoes in the second echo set with respect to the echo center of the echo period. A first echo image set is obtained based on first echo set data acquired in each echo period, and a second echo image set is obtained based on second echo set data acquired in each echo period. Using the first and second echo image sets, a Dixon water-fat separation calculation is performed to obtain a water image and a fat image. The method and device can advantageously increase acquisition efficiency and the signal-to-noise ratio.

In a MR water-fat image separation method and device, within one echo period, a first echo set under a first readout gradient polarity and a second echo set under a second readout gradient polarity are acquired. The first and second readout gradient polarities may be opposite, and echoes in the first echo set may be positionally one-to-one symmetric to echoes in the second echo set with respect to the echo center of the echo period. A first echo image set is obtained based on first echo set data acquired in each echo period, and a second echo image set is obtained based on second echo set data acquired in each echo period. Using the first and second echo image sets, a Dixon water-fat separation calculation is performed to obtain a water image and a fat image. The method and device can advantageously increase acquisition efficiency and the signal-to-noise ratio.

A method and device for calculating a river surface flow velocity are provided based on a variational principle, which are used to capture and process the images of an objective area, and to obtain the flow velocity field data of the objective area with high precision in a non-contact manner. The method and device include 3 steps: (1) preparation before initial flow measurement; (2) capturing a video of the river by an image acquisition device, converting a motion of a pixel flow field of the fluid in a captured image sequence into solving an energy functional optimization problem, and solving partial differential equations to obtain data of pixel flow field distribution; and (3) obtaining space coordinates of the pixel point in a world coordinate system and calculating the flow velocity according to the data obtained in the step 2 and the transformation relationship determined in the step 1.

A method and device for calculating a river surface flow velocity are provided based on a variational principle, which are used to capture and process the images of an objective area, and to obtain the flow velocity field data of the objective area with high precision in a non-contact manner. The method and device include 3 steps: (1) preparation before initial flow measurement; (2) capturing a video of the river by an image acquisition device, converting a motion of a pixel flow field of the fluid in a captured image sequence into solving an energy functional optimization problem, and solving partial differential equations to obtain data of pixel flow field distribution; and (3) obtaining space coordinates of the pixel point in a world coordinate system and calculating the flow velocity according to the data obtained in the step 2 and the transformation relationship determined in the step 1.

An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.

An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.