Liver capsule wire automatic extracting method based on liver ultrasonic image
An ultrasound image, automatic extraction technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as limited value, misdiagnosis of subjective factors, and missed the best time for treatment
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Embodiment 1
[0101] Such as figure 1 As shown, the method for automatically extracting the liver capsule line based on the liver ultrasound image in this embodiment may include the following steps:
[0102] Step 101, for the ultrasound image to be processed including liver sections / parts, use a sliding window detector to process the ultrasound image, and establish multiple channels in the image block corresponding to the window of the sliding window detector, from the established multiple Extract the pre-selected random rectangular features from channels to obtain the detection response map.
[0103] In this embodiment, the random rectangle features are determined in advance through training samples.
[0104] The ultrasound image in this embodiment may be a pre-acquired test ultrasound image / test image, or a grayscale image obtained in other ways.
[0105] It should be noted that the liver capsule line is extracted in this embodiment, and the above-mentioned ultrasound images are mainly ...
Embodiment 2
[0176] Such as Figure 4 As shown, the method for automatically extracting the liver capsule line of the present embodiment includes the following steps:
[0177] Step 401 , for all training ultrasound images, manually mark the envelope line, uniformly sample and extract image blocks on the envelope line as positive samples, and randomly sample and extract image blocks at other positions of the image as negative samples. Extract a variety of features for positive and negative samples, combine the features and reduce the dimensionality, and train the support vector machine.
[0178] Wherein, the various features are Histogram of Gradients (HOG), Local Binary Pattern (LBP) and Deep Convolutional Neural Network (CNN) features. The structure of CNN is as Figure 5 shown. Figure 5 It is the processing process of the convolutional neural network, and the numbers in the figure are the numbers used in the processing of the convolutional neural network, which are not limited in thi...
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