Please select the most similar patent number from A, B, C and D. Which number is?
The present application is applicable to the technical field of image processing, and provides a quantitative detection method and detection apparatus for the degree of cerebral atrophy, and a terminal device. The method comprises: obtaining a first preset number of first template images and target values of the first template images, and marking the target values of the first template images as first target values, the first templates images being brain magnetic resonance images of individuals of preset ages having healthy brains; obtaining a brain magnetic resonance image of an individual to be tested, obtaining an image to be tested, and calculating target values of the image to be tested to obtain second target values; according to the first target values, calculating percentiles of the second target values, and obtaining the degree of cerebral atrophy of the individual to be tested. The described method quantifies the degree of cerebral atrophy, for effective evaluation of the degree of cerebral atrophy.
A: The present invention relates to the computer vision field. Disclosed are a method and a device for extracting image features. The method comprises: performing gray scale equalization processing on an image to be processed, and selecting a preset number of point pairs from the image that is processed; determining two extraction areas by using two points in a first point pair as centers; obtaining a color quantization matrix and a gradient matrix corresponding to each extraction area; fusing the color quantization matrix and the gradient matrix corresponding to each extraction area to obtain two fusion matrixes; calculating, according to the two fusion matrixes, a scalar value corresponding to the first point pair; calculating scalar values of other point pairs in the same way; and combining scalar values of the preset number of point pairs to obtain a feature vector of the image to be processed. A gradient value can describe a target outline of an image, and a color relates to an object and a scene contained in the image, featuring low dependency on the image and high robustness. Therefore, the present invention uses a gradient value and a color to extract an image feature, which can improve accuracy of the image feature that is extracted.
B: A lesion part identification method, identification apparatus, computer apparatus and readable storage medium. The method comprises: acquiring a first magnetic resonance image and a second magnetic resonance image by performing magnetic resonance scanning on a preset part of human body by applying different magnetic resonance scanning sequences (101); performing image registration on the first magnetic resonance image and the second magnetic resonance image (102); detecting the registered first magnetic resonance image and second magnetic resonance image by using a trained regression convolutional neural network model to obtain a first preset part area in the registered first magnetic resonance image and a second preset part area in the registered second magnetic resonance image (103); predicting the probability that the first preset part area belongs to a lesion area by using a first convolutional neural subnetwork in a trained dual training convolutional neural network model so as to obtain a first lesion probability, and predicting the probability that the second preset part area belongs to a lesion area by using a second convolutional neural subnetwork in the trained dual training convolutional neural network model so as to obtain a second lesion probability (104); and determining whether the preset part is a lesion part according to the first lesion probability and the second lesion probability (105). The present method may quite accurately identify lesion parts.
C: The present application is applicable to the technical field of image processing. Provided are a facial detection method and apparatus, and a terminal device and a computer-readable storage medium. The facial detection method comprises: acquiring an image to be subjected to detection, wherein a first facial image is present in said image; performing preliminary detection on said image, so as to obtain a preliminary detection result; if the preliminary detection result indicates that the detection is passed, comparing the first facial image in said image with a target facial image, so as to obtain a comparison result; an determining a final detection result of said image according to the comparison result. By means of the method, the accuracy of facial detection can be effectively improved.
D: The present application relates to the technical field of sensors, and disclosed thereby are a target detection method and device, which guarantee the reliability of a target detected to thereby improve the accuracy of target detection. The target detection method comprises: according to a first confidence and a second confidence, acquiring a mixed confidence when a first target and a second target are the same target, wherein the first confidence is the reliability of the first target in a first image, the second confidence is the reliability of the second target in a second image, the first image is from a first image capture device, the second image is a surround-view image obtained from images from multiple image capture devices, and the multiple image capture devices comprise the first image capture device; and according to the mixed confidence, outputting a target detection result. The described method and device may be used for target detection and tracking during assisted driving and self driving.
Answer: B