(英)显微高光谱成像的皮肤黑色素瘤表面扩散深度识别方法研究
投稿时间:2020-03-18  修订日期:2020-05-06  点此下载全文
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作者单位E-mail
王健生 华东师范大学上海市多维度信息处理重点实验室 6248954@qq.com 
李庆利 华东师范大学上海市多维度信息处理重点实验室 qlli@cs.ecnu.edu.cn 
周梅 华东师范大学上海市多维度信息处理重点实验室  
孙力 华东师范大学上海市多维度信息处理重点实验室  
胡孟晗 华东师范大学上海市多维度信息处理重点实验室  
吕岳 华东师范大学上海市多维度信息处理重点实验室  
褚君浩 华东师范大学上海市多维度信息处理重点实验室  
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:皮肤黑色素瘤是一种严重的恶性肿瘤,其发病率和死亡率呈逐年上升趋势。而对于黑色素瘤的准确诊断有助于对其治疗。在诊断中皮肤黑色素瘤的表面扩散深度是一项重要的指标,现有的检测方法多是基于普通彩色图像而且较大程度依赖于医生的经验。本论文研究了基于显微高光谱图像的皮肤黑色素瘤表面扩散深度检测方法。该方法首先使用核最小噪声分割、形态滤波和边缘检测实现颗粒层的分割,然后使用基于特征谱监督算法的最小二乘支持向量机实现恶性黑素细胞的检测,最后定量化计算了皮肤黑色素瘤的扩散深度。实验结果表明,本论文的方法可以为皮肤黑色素瘤的扩散提供定量化的参考指标,有利于皮肤黑色素瘤的准确诊断。
中文关键词:显微高光谱成像  黑色素瘤  表面扩散深度  图像分割
 
Identification and Measurement on Cutaneous Melanoma Superficial Spreading Depth Using Microscopic Hyperspectral Imaging Technology
Abstract:Cutaneous melanoma is a serious malignant tumor with morbidity and mortality increasing year by year. Accurate diagnosis can greatly improve the curativeness of patients. The existing ways for melanoma pathological diagnosis rely on microscopic images and place high demands on pathologists’ experience. In recent decades, researchers are aiming to explore more advanced pathology-assisted diagnostic techniques for cutaneous melanomas. This paper presents an automatic approach for measurement of superficial spreading depth of cutaneous melanomas based on microscopic hyperspectral imaging technology. To extract skin granular layer, an edge detection method combined with kernel minimum noise fraction is proposed. Then least squares support vector machine based on characteristic spectrum supervision is used to identify malignant melanocytes. The measurement of tumor superficial spreading depth depends on the vertical distance from the skin granular layer to the deepest malignant melanocytes. Experimental results illustrate that the proposed method is possible to provide an effective reference for diagnosis and treatment of cutaneous melanoma.
keywords:microscopic hyperspectral image, cutaneous melanoma, superficial spreading depth, segmentation
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