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Medical image fusion method and image detection method based on fusion medical image learning
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A medical imaging and fusion method technology, applied in the field of deep learning, can solve the problems of small differences in tissue grayscale, affecting the application of medical imaging, and unable to provide information on diseased tissue.
Active Publication Date: 2018-12-18
HARBIN UNIV OF COMMERCE
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[0003] The purpose of the present invention is to provide a medical image fusion method and an image detection method based on fusion medical image learning to solve the problem that medical images are affected by noisepollution, low signal-to-noise ratio, and small grayscale differences between different tissues. application, and technical issues such as single modality images cannot provide richer information on diseased tissue from different angles
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specific Embodiment approach 1
[0061] Specific implementation mode one: as figure 1 As shown, the medical image fusion method described in this embodiment includes
[0062] (1), read mode A medical image I A , modality B medical image I B ;
[0063] (2) Preprocess the two types of modal medical images respectively to obtain the denoised image I q ;
[0064] (3), using the improved shearlet transform to carry out multi-scale subdivision of the image;
[0065] (4), according to the fusion rules, the two types of modal images are fused to obtain the fused image I F ;
[0066] In step (2), a guided filtering algorithm is used for medical image preprocessing, and the specific process is as follows;
[0067] The input parameters of the guidance filter are the guidance image I and the image p (input medical image) that needs to be processed and optimized, and the output is the optimized image q. The guide image and the input image can be preset as I=p, both of which are original medical images. It is deri...
specific Embodiment approach 2
[0100] Specific implementation mode two: as figure 1 As shown, the image detection method based on fusion medical image learning described in this embodiment includes:
[0101] (1), read mode A medical image I A , modality B medical image I B ;
[0102] (2) Preprocess the two types of modal medical images respectively to obtain the denoised image I q ;
[0103] (3), using the improved shearlet transform to carry out multi-scale subdivision of the image;
[0104] (4), according to the fusion rules, the two types of modal images are fused to obtain the fused image I F ;
[0107] (7) Use the trained network to detect the image to be detected, and give a judgment decision.
[0108] In step (6), the YOLO v2 deep learning algorithm is used to train the fused image data set obtained ...
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Abstract
The invention relates to a medical image fusion method and an image detection method based on fusion medical image learning, which relate to an image detection technology based on fusion medical imagelearning. The invention solves the technical problems that the medical image is polluted by noise, the signal-to-noise ratio is low, the gray level difference between different tissues is small, theapplication of the medical image is affected, and the single mode image cannot provide richer information of the pathological tissue from different angles, and the like. The method comprises the stepsof: reading the two kinds of modal images and preprocessing the two kinds of modal images respectively to obtain denoised images; using improved shearing wave transform for multi-scale image segmentation. According to the fusion rules, two kinds of modal images are fused to get fused images. All the fused images are combined into a fused image data set. The improved YOLO v2 depth learning algorithm is used to train the dataset, and the training network is generated. The method performs detection with a trained network. Different modalities of medical images are fused together to provide moreinformation from different perspectives.
Description
technical field [0001] The invention belongs to the field of deep learning, and in particular relates to an image detection technology based on fusion medical image learning. Background technique [0002] More than 90% of medical data come from medical images, including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), etc. Advanced medical image detection technologies are used in various diseases, especially tumors. It plays an important role in the detection and diagnosis of diseases. However, as a screening procedure for early diagnosis of tumors, current imaging diagnosis mainly relies on manual work, requiring one or more experienced doctors to check for signs of lesions and make a diagnosis. In the era of medical big data, the growing number of images The data brings great difficulties to manual image reading, which is not only expensive and time-consuming, but also due to the heavy workload and fatigue of doct...
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