Multi-layer consistency and coordination method for brain image medical record feature extraction
A feature extraction and brain imaging technology, applied in the field of intelligent processing of medical information, can solve problems such as low precision, time-consuming, difficult training, etc.
Active Publication Date: 2018-08-24
NANTONG UNIVERSITY
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Problems solved by technology
Due to the internal similarity of brain imaging medical records, it is difficult to distinguish fine-grained images. Existing algorithms have problems such as time-consuming, difficult training and low precision in feature extraction of brain imaging medical records. We need to further propose some An efficient method to reveal the imaging neural mechanism and internal structure of brain diseases, improve its classification accuracy, and provide objective indicators for the diagnosis and evaluation of brain diseases
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[0068] Such as Figure 1-Figure 4 A specific implementation of a multi-layer consistent collaboration method for brain imaging medical record feature extraction is shown: including the following steps:
[0069] A. Design an efficient multi-layer collaborative MapReduce model, construct the preference relationship matrix PE between each elite and the adjacency matrix P, and perform multi-layer correlation feature identification on the multiple related indivisible brain imaging medical records in the collaborative meme group, and ask Draw out the elite transfer matrix ECM and minimize its feature vector Cov; specifically including the following steps:
[0070] a. Construct the i-th Memeplex of the evolutionary population i Parallel Map / Reduce operation operatori ,value i >, the specific definition is as follows:
[0071]
[0072]
[0073] Where f Elitisti Memeplex for the i-th meme group i The local optimal fitness of the middle elite, f ELITIST Is the minimum fitness of all elites i...
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The present invention discloses a multi-layer consistency and coordination method for brain image medical record feature extraction. The method comprises the steps of: constructing a multi-layer coordination MapReduce model to perform identification of indivisible related brain image medical record features and perform effective classification of brain medical records with a plurality of related features; designing a brain image medical record feature consistency compatibility polymerization method to allow a local solution and a global dominance solution, which are extracted by a coordinationmeme set, of the brain image medical record features to achieve effective equilibrium; employing a multi-decision consistency optimization matrix to further detect a non-cooperative MapReduce behavior of the meme set so as to effectively obtain consistency Nash equilibrium of the feature set; and finally, assessing the precision of extraction of the brain image medical record features, and outputting an optimal feature selection set. The multi-layer consistency and coordination method provides important image feature basis for clinical diagnosis and treatment of related diseases.
Description
Technical field: [0001] The present invention relates to the field of intelligent processing of medical information, in particular to a multi-layer consistent collaboration method for extracting features of brain imaging medical records. Background technique: [0002] The image structure of brain imaging medical records is extremely complex, and many features are not obvious. It has the characteristics of huge amount of information, diverse formats, fast access speed and high application value. Currently, there are no objective indicators for early diagnosis of hidden disease symptoms in brain imaging medical records. , Risk assessment and treatment options, etc. Brain image feature extraction mainly involves complex information analysis models and methods. At present, magnetic resonance brain imaging is mainly used to provide functional and structural image information, and to detect brain functional activity characteristics, functional and structural network characteristics, et...
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CPCG06F18/232G06F18/2133
Inventor 丁卫平陆琰管致锦王杰华陈森博董建成程学云张晓峰胡彬沈学华余利国景炜张琼
Owner NANTONG UNIVERSITY