Method for detecting object of esophageal cancer in hyperspectral imaging
A method for detecting objects in hyperspectral imaging is revealed. First obtaining a hyperspectral imaging information by a reference image. Then converting an input image according to the hyperspectral imaging information to get a hyperspectral image. A plurality of hyperspectral eigenvalues is obtained after image analysis of the hyperspectral image. Next getting a plurality of dimensionality reduction eigenvalues by a principal component analysis (PCA). Then performing convolution operation on the dimensionality reduction eigenvalues to get a value of a convolution matrix for extracting a feature image from an image of an object to be detected in the input image. Generating an anchor box and a prediction box in the feature image to get a bounded image. Lastly matching and comparing the bounded image with a sample image to determine whether the input image is a target object image. Thereby the method provides assistance for physicians in gastrointestinal image diagnosis.
1 . A method for detecting objects in hyperspectral imaging, comprising, in a host:
obtaining hyperspectral imaging information relating to a reference hyperspectral image, the reference hyperspectral image having been converted from a reference image captured by an endoscope;
obtaining an input image captured by the endoscope;
converting the input image based on the hyperspectral imaging information to obtain a hyperspectral image of the input image;
executing an image analysis of the hyperspectral image of the input image to obtain a plurality of hyperspectral eigenvalues from the hyperspectral image of the input image;
executing a principal component analysis (PCA) to simplify the plurality of hyperspectral eigenvalues and obtain a plurality of dimensionality reduction eigenvalues from the simplified hyperspectral eigenvalues;
obtaining a value of a convolution matrix based on the dimensionality reduction eigenvalues, a kernel, and at least one convolutional layer, wherein the kernel includes a plurality of feature weight parameters, and the value of the convolution matrix is obtained by multiplying the plurality of feature weight parameters of the kernel with the plurality of dimensionality reduction eigenvalues;
extracting a plurality of feature images from the input image based on the value of the convolution matrix;
executing zooming, cropping, and arrangement of the plurality of feature images to connect the plurality of feature images and form a joint image;
generating a plurality of grid cells based on the joint image;
setting at least one anchor box on the input image based on the plurality of grid cells and extracting a plurality of positioning parameters corresponding to the plurality of feature images;
setting at least one prediction box on the input image based on the plurality of positioning parameters;
obtaining at least one bounded image from the plurality of feature images based on the prediction box;
comparing the at least one bounded image with at least one sample image to generate a comparison result; and
determining whether the input image is a target object image or not based on the comparison result.
2 . The method as claimed in claim 1 , wherein the host compares the bounded image with the sample image to generate the comparison result by an object detection algorithm YOLOv5.
3 . The method as claimed in claim 1 , wherein the hyperspectral imaging information includes a plurality of color matching functions, a correction matrix, and a conversion matrix each corresponding to the input image.
4 . The method as claimed in claim 1 , wherein the host reads the sample image from a database for comparing with the at least one bounded image.
5 . The method as claimed in claim 1 , wherein the host sets the plurality of grid cells on the input image for positioning the anchor box based on the grid cells, and the anchor box corresponds to at least one ratio between a length and a width.
6 . The method as claimed in claim 1 , a plurality of prediction boxes is generated by the host respectively based on a plurality of anchor boxes of different target sizes.
7 . The method as claimed in claim 1 , wherein the host aligns a central coordinate of the at least one prediction box based on a central coordinate of the anchor box and approaches a bounding coordinates of the at least one prediction box based on an aspect ratio of the anchor box.
8 . The method as claimed in claim 7 , wherein the host sets the at least one prediction box in a plurality of scales including ⅛, 1/16, 1/32, and a combination thereof based on an object detection algorithm YOLOv5.
9 . The method as claimed in claim 1 , wherein in the principal component analysis (PCA), the host extracts a maximum variance based on a hyperspectral vector to which the hyperspectral eigenvalues correspond and then the dimensionality reduction eigenvalues are generated.
10 . The method as claimed in claim 1 , wherein the image analysis extracts a plurality of feature points in the hyperspectral image to obtain the plurality of hyperspectral eigenvalues.
11 . The method as claimed in claim 1 , wherein an object to be detected in the input image is bounded by the at least one prediction box.