Image-based early diagnosis system for senile dementia

Abstract

The invention discloses an image-based early diagnosis system for senile dementia. The early diagnosis system comprises a brain image collector (1) and a memorizer (2) and is characterized by comprising image preprocessing equipment (3), an image asymmetry characteristic extraction device (4), a consolidator (5), a characteristic vector selector (6), an optimization classifier (7) and an output device. The early diagnosis system has the remarkable effect of being capable of obviously improving the accuracy of early diagnosis of the senile dementia when being applied to the early diagnosis of the senile dementia. The early diagnosis system not only can be used for realizing the clinical application of sequential asymmetry characteristics of anatomical structures in the brain MR (Magnetic Resonance) images in the early diagnosis of the senile dementia, but also is high in diagnosis accuracy and real-time performance.

Description

Technical field [0001] The invention belongs to a disease diagnosis system, in particular to an early diagnosis system for images of Alzheimer's disease. Background technique [0002] The brain MR image processing method is a powerful tool for studying the early diagnosis of Alzheimer's disease, and it has achieved certain positive results. Among them, obtaining high-quality biological characteristics is the key to the success of this method. Medical research has found that the asymmetry of anatomical structures in brain MR images is closely related to the early evolution of Alzheimer's disease. Asymmetry features can characterize the early brain pathological process and help achieve early diagnosis. In recent years, some scholars at home and abroad have begun to study the role of the asymmetry of brain anatomy in the early diagnosis of Alzheimer's disease. The anatomical structures involved are: hippocampus, lateral ventricle, gray matter, white matter, neurocortex, etc. The ...

AI Technical Summary

Problems solved by technology

But these studies also have some limitations: First, the asymmetry of some brain anatomical structures has been studied sporadically, and all related brain anatomical structures have not been systematically studied. For example, the parietal lobe and frontal lobe have been medically proven to be related to Alzheimer's disease, but its asymmetry has not been studied

This will lead to a mismatch between the optimal feature subset and the optimized parameters, which is not conducive to obtaining the optimal classification accuracy (ie, disease diagnosis accuracy)

These deficiencies greatly limit the use of machine learning in the selection of asymmetric features of anatomical structures in brain MR images.

[0008] The disadvantage of the existing technology is: based on the above literature analysis, a general conclusion has been formed: the asymmetrical changes profoundly reflect the decline of function during the evolution of senile dementia, and are closely related to early brain lesions

However, the current research on asymmetry features is only carried out on images of a single time state, and has not solved how to systematically and comprehensively obtain the optimal combination of asymmetry features for early diagnosis of Alzheimer's disease, which greatly hinders its clinical application. application development

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