RF和C4.5決策樹在食管癌圖像分類中的研究

茹仙古麗·艾爾西丁　木拉提·哈米提　嚴傳波

摘 要：目的 探討RF和C4.5決策樹對X線食管造影圖像分型中的應(yīng)用，以及驗證分類器對特征的分類能力。方法 選取2018年1月～6月在新疆醫(yī)科大學第一附屬醫(yī)院、第二附屬醫(yī)院和第三附屬（腫瘤）醫(yī)院的放射科選取潰瘍性、縮窄型和蕈傘型食管癌X線圖像各560張，提取灰度共生矩陣，灰度直方圖和混合特征；采用RF和C4.5決策樹通過調(diào)整參數(shù)進行分類研究。結(jié)果 RF和C4.5決策樹對潰瘍型和縮窄型食管癌進行分類，灰度共生矩陣的分類準確率分別為73.30%，67.76%；灰度直方圖分類準確率分別為84.55%，76.16%。而混合特征算法的分類準確率分別為95.08%，86.87%；對潰瘍型和蕈傘型食管癌進行分類，灰度共生矩陣的分類準確率分別為75.08%，66.96%；灰度直方圖分類準確率分別為83.83%，77.23%。而混合特征算法的分類準確率分別為80.98%，73.66%。結(jié)論 灰度直方圖特征的分類準確率比灰度共生矩陣特征的平均高10%，混合特征更適合于潰瘍型，縮窄型食管癌的分類。而灰度直方圖特征更適合于潰瘍型，蕈傘型食管癌的分類；RF的分類能力比C4.5決策樹高。此算法可為X線食管造影圖像的分類提供參考。

關(guān)鍵詞：食管癌；隨機森林；C4.5決策樹；特征提取

中圖分類號：R735.1；TP391.4 文獻標識碼：A DOI：10.3969/j.issn.1006-1959.2018.22.015

文章編號：1006-1959（2018）22-0051-05

Research on RF and C4.5 Decision Tree in Image Classification of Esophageal Cancer

Roxangvl·Arxidin1，Murat·Hamit2，YAN Chuan-bo2，YAO Juan3

（1.Basic Medical College，Xinjiang Medical University，Urumqi 830011，Xinjiang，China；

2.College of Medical Engineering Technology，Xinjiang Medical University，Urumqi 830011，Xinjiang，China；

3.Department of Radiology，the First Affiliated Hospital，Xinjiang Medical University，Urumqi 830054，Xinjiang，China）

Abstract：Objective To explore the application of RF and C4.5 decision tree to the classification of X-ray esophageal images and to verify the classifier's ability to classify texture features.Methods From January to June 2018， the radiologists of the first affiliated Hospital， the second affiliated Hospital and the third affiliated （tumor） Hospital of Xinjiang Medical University selected 560 X-ray images of ulcerative， constrictive and mushroom esophageal cancer to extract the gray level symbiosis matrix. Grayscale histogram and mixed feature； RF and C4.5 decision tree are used to study the classification by adjusting the parameters.Results RF and C4.5 decision tree were used to classify ulcerative and constricted esophageal cancer. The classification accuracy of gray co-occurrence matrix was 73.30%and 67.76%.The classification accuracy of gray histogram was 84.55% and 76.16%，respectively.The classification accuracy of comprehensive feature algorithm was 95.08% and 86.87%， the classification accuracy of ulcerative and mushroom esophageal cancer was 75.08% and 66.96%， respectively， and the classification accuracy of gray histogram was 83.83%and 77.23%， respectively. The classification accuracy of comprehensive feature algorithm was 80.98% and 73.66%，respectively.Conclusion The classification accuracy of grayscale histogram is 10% higher than that of gray level co-occurrence matrix. The comprehensive feature is more suitable for classification of ulcerative and constrictive esophageal cancer. The gray histogram features are more suitable for the classification of ulcerative and mushroom esophageal cancer， and the classification ability of RF is higher than that of C4.5 decision tree. This algorithm can provide reference for the classification of X-ray esophageal images.

Key words：Esophageal cancer；Random forest；C4.5 decision tree；Feature extraction

癌癥是嚴重危害人類健康的慢性疾病，也是威脅生命的主要殺手。其中食管癌是對癌癥患者生存質(zhì)量（quality of life，QOL）影響最大的疾病之一[1]。新疆哈薩克族是食管癌的高發(fā)民族，其食管癌死亡率達155.9/106，高于我國平均水平15.23/106，是本地區(qū)重點防治的惡性腫瘤[2]?！?br>