Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Abstract: According to one embodiment, a detection apparatus includes a processor. The processor acquires height information at a plurality of points in a subject. The processor determines whether or not one or more steps are present at a height equal to or higher than a predetermined height from a reference in the subject based on the height information. The processor detects that the subject is in a state in which a plurality of objects are overlapped, if the number of the steps is equal to or greater than a first threshold value.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: According to one embodiment, a wall detection device includes an acquirer and processing circuitry. The acquirer acquires a point cloud, which includes a series of coordinates of a plurality of points corresponding to a first wall and a second wall that oppose each other. The processing circuitry detects a first detected wall and a second detected wall based on a model and the acquired point cloud, the model representing a first plane which corresponds to the first detected wall and indicates a surface of the first wall and a second plane which corresponds to the second detected wall and indicates a surface of the second wall, and the model representing the first plane and the second plane being parallel to each other.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry inputs a noise correlation map and a medical image or an intermediate image to a learned model that is functioned to generate a denoise image, in which noise of the medical image or noise of the intermediate image is reduced, based on the medical image generated based on data collected with respect to a subject or the intermediate image at a front stage for generating the medical image and the noise correlation map correlated with the noise included in the medical image or the intermediate image, and generates the denoise image, in which the noise of the medical image or the noise of the intermediate image is reduced.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry inputs a noise correlation map and a medical image or an intermediate image to a learned model that is functioned to generate a denoise image, in which noise of the medical image or noise of the intermediate image is reduced, based on the medical image generated based on data collected with respect to a subject or the intermediate image at a front stage for generating the medical image and the noise correlation map correlated with the noise included in the medical image or the intermediate image, and generates the denoise image, in which the noise of the medical image or the noise of the intermediate image is reduced.

Abstract: According to one embodiment, a distance measuring apparatus includes a processing circuit and a memory. The processing circuit generates a distance image by measuring distances to an obstacle by using the time until the emitted light is reflected from the obstacle and returned as reflected light. The processing circuit generates an intensity image by measuring the intensity of the reflected light or the intensity of the environmental light. The memory stores a learned model for generating a denoise image, in which noise of the distance image is reduced, based on the distance image and the intensity image.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry is configured to apply a filter to each of a first real-part image and a first imaginary-part image of a first complex image generated based on acquired magnetic resonance data and thereby generate a second complex image that includes a second real-part image and a second imaginary-part image. The processing circuitry is configured to generate a phase image denoised by the filter, the denoised phase image generated based on the second real-part image and the second imaginary-part image. The processing circuitry is configured to generate an intensity image related to an absolute value of the first complex image based on pixel values of the denoised phase image, the first real-part image, and the first imaginary-part image.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry is configured to apply a filter to each of a first real-part image and a first imaginary-part image of a first complex image generated based on acquired magnetic resonance data and thereby generate a second complex image that includes a second real-part image and a second imaginary-part image. The processing circuitry is configured to generate a phase image denoised by the filter, the denoised phase image generated based on the second real-part image and the second imaginary-part image. The processing circuitry is configured to generate an intensity image related to an absolute value of the first complex image based on pixel values of the denoised phase image, the first real-part image, and the first imaginary-part image.

Abstract: According to one embodiment, a signal processing apparatus includes a memory and a processing circuit. The memory stores a learned model for generating restoration data by restoring deterioration of a signal based on data of the signal and data related to a position of a sample of the signal. The processing circuit inputs data of the signal and data related to the position to the learned model. The processing circuit generates the restoration data by using the learned model.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry inputs a noise correlation map and a medical image or an intermediate image to a learned model that is functioned to generate a denoise image, in which noise of the medical image or noise of the intermediate image is reduced, based on the medical image generated based on data collected with respect to a subject or the intermediate image at a front stage for generating the medical image and the noise correlation map correlated with the noise included in the medical image or the intermediate image, and generates the denoise image, in which the noise of the medical image or the noise of the intermediate image is reduced.

Abstract: According to one embodiment, a distance measuring apparatus includes a processing circuit and a memory. The processing circuit generates a distance image by measuring distances to an obstacle by using the time until the emitted light is reflected from the obstacle and returned as reflected light. The processing circuit generates an intensity image by measuring the intensity of the reflected light or the intensity of the environmental light. The memory stores a learned model for generating a denoise image, in which noise of the distance image is reduced, based on the distance image and the intensity image.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Abstract: An image processing apparatus includes an operating unit configured to calculate a tangent-line direction of an edge and a normal-line direction of the edge by using a vertical-direction derivative value and a horizontal-direction derivative value of a pixel value of a pixel in an input image; a converting unit configured to convert local coordinates positioned within a predetermined area with respect to a target pixel in the input image into rotated coordinates, by rotating the local coordinates according to an angle formed by the horizontal direction and the tangent-line direction of the edge; and a fitting unit configured to perform, at the rotated coordinates, a fitting process that employs a least-squares method by using a curved surface model expressed with the pixel value of the target pixel in the input image.