Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation...Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.展开更多
文摘Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.
文摘目的研究川崎病(Kawasaki disease,KD)患儿急性期血脂水平与冠脉病变关系,探讨是否可以将血脂水平作为川崎病监测指标。方法回顾性分析2020年1月—2022年7月福建医科大学附属三明第一医院儿科收治的47例KD患儿资料,根据心脏彩超检查冠脉情况结果分为冠脉病变组(n=23)和冠脉正常组(n=24),同时进行血脂4项检测:三酰甘油(triglyceride,TG)、总胆固醇(total cholesterol,TC)、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)。比较两组患儿TG、TC、LDL-C、HDL-C有无差异。结果KD患儿急性期(1~9 d病程)冠脉病变组测定的血TG、TC、LDL-C及HDL-C与冠脉正常组比较,差异有统计学意义(P<0.05)。结论KD患儿急性期血脂水平可作为早期诊断KD患儿并发冠脉病变的重要指标之一,具有理想的应用价值。
