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|標題:||MAP2 Antibody Staining Rat Brain Tissue Image Based Stroke Stage Diagnostic Method|
以 MAP2 抗體染色之老鼠腦組織影像為基礎之中風診斷方法
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|摘要:||Stroke, also known as cerebral vascular accident, has rank second on the top ten causes of death worldwide in the past decade. Interrupted blood supply due to blood vessel blockage or sudden burst of blood vessels can cause brain damage and thereby result in long-term effects or even death. Out of all stroke incidents, blood vessel blockage, which is referred to as ischemic stroke accounts for approximately 87%. Therefore, there are increasing studies on a better understanding of this disease and on developing improved treatment.
Animal models are used to better understand stroke by simulating the pathophysiological changes in human stroke, rodents especially rats are the most commonly used stroke models. In this research, images from animal models of ischemic stroke carried out in rats are the basis of the proposed method.
In this research, an automatic stroke diagnostic method was proposed. The method firstly extracts image features by using gray-level co-occurrence matrix (GLCM) and Tamura. Then the method trains these features by using genetic algorithm and k-means clustering algorithm to obtain the stroke diagnosis model. Using this model, we can recognize the stroke stage of testing rats. The overall experimental results indicate that the proposed method achieves good performance in recognition of different stroke stage images. On top of that, a proofed effective stroke treatment carried out in experiment is used to verify the proposed method, the experimental results also demonstrates well performance.|
腦中風(stroke)，又稱為腦血管意外(cerebral vascular accident)，近年來在全球十大死因中高居第二。中風起因於腦血管阻塞或爆裂所造成的腦部破壞，使患者留下長期後遺症甚至是死亡。腦血管阻塞又稱為缺血性中風(ischemic stroke)，在所有的中風事件中，其發生機率約略為 87%。因此，現今已有更多的研究投入於更深入了解中風疾病，並且發展更妥善的治療方法。在醫學實驗中 動物模型常被用於模擬疾病在人體可能產生的病理生理變化。，而在腦中風的動物實驗中，經常使用囓齒動物特別是老鼠進行研究。本研究的實驗影像即為以老鼠為主之缺血性腦中風動物模型。本研究所提出的自動化中風階段診斷方法是基於影像處理的方法。此方法首先經由灰階共現矩陣(GLCM)及 Tamura 方法針對不同階段中風影像擷取影像特徵 將影像特徵值透過基因演算法結合 K-means 分群演算法訓練中風診斷模型，，利用不同階段中風影像的不同特徵作為判斷中風嚴重程度的依據。實驗結果顯示，本方法在診斷中風階段有良好的結果，並且透過已知有效的治療中風藥物也能驗證本方法的有效性。
|Appears in Collections:||資訊管理學系|
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