Cancer Detection of Mucosa Tissues by Epithelium and Lamina Propria Classification Based on Hyperspectral Imaging System (HIS)
|關鍵字:||癌症檢測;超頻譜影像系統;分類;黏膜組織;上皮組織;固有層;形態分析;Cancer Detection;Hyperspectral Imaging System;Classification;Mucosa;Epithelium;Lamina propria;Morphological analysis|
首先，影像中的細胞核作為主要的判別目標，細胞核的光譜資訊抽取出來後以主成分分析(PCA)與費雪線性判別分析(Fisher's linear discriminant)進行上皮組織與固有層組織的鑑別訓練，最後根據訓練資料，將所有細胞核以單純貝氏分類器(NBC)、K最鄰近演算法(KNN)與支持向量機(SVM)進行分類。將所有細胞核分類完成後，為量化病理學上癌化細胞的變異，細胞核的熵 (Entropy)用以計算細胞核的亂度與生長異常、細胞的碎形維度(fractal dimension)用以計算上皮組織的破碎程度、最後再以型態學影像分析估算兩種組織的細胞混合程度，結合這三種方法可以準確的偵測出癌化的切片樣本。
Malignant neoplasm (also known as cancer) has been the main cause of death for decades in both developing and developed countries. As such, cancer has become an economic burden and a huge national healthcare issue for all countries affected by this disease. Globally, 12.7 million cancer cases and 7.6 million cancer deaths were evaluated in 2008. Cancer detection and screening have been important issues for decades. In this study, a unique embedded relay lens hyperspectral imaging system was used for cancer detection in mucosa tissues, with oral mucosa tissues being obtained for the experiment. The analysis used both spectral profiles and spatial information to judge the experimental samples. All nuclei in the images were identified; the feature profiles of the hyperspectral training data were extracted by principal component analysis (PCA) and Fisher’s linear discriminant; and all nuclei were recognized by three classifiers. According to fundamental pathological changes in cancerous mucosa tissue, three methods were proposed to distinguish between healthy and cancerous tissue. The entropy of the nuclei was calculated for measuring the nuclei changes, and the fractal dimension was calculated as a measurement of completeness of the epithelial tissue. A combination or mixture of two classes of nuclei was also evaluated using morphological imaging processes. By combining the three methods, the defects of each method could be redressed by consulting the two other methods. There were two final results due to two sets of discrimination thresholds being chosen based on different methods. The sensitivity and specificity of the final results were 97.06% and 88.24% or 94.12% and 91.18%.
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