MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

Giotis Ioannis; Molders Nynke; Land Sander; Biehl Michael; Jonkman Marcel F.; Petkov Nicolai

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MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

机译：MED-NODE：使用非皮层图像的计算机辅助黑色素瘤诊断系统

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Melanoma is one of the most aggressive types of skin cancer and in many cases it is difficult to differentiate from benign naevi. In this contribution we present a decision support (expert) system, which we call MED-NODE, able to assist physicians with this challenging task. The proposed system makes use of non-dermoscopic digital images of lesions from which it automatically extracts the lesion regions and then computes descriptors regarding the color and texture. In addition, a set of visual attributes is provided by the examining physician. The automatically extracted descriptors and the attributes provided by the physician are separately used for automatic prediction. Final classification is achieved by a majority vote of all predictions. The proposed system achieves high diagnostic accuracy results (81%) and performs comparably to state-of-the-art methods that are using dermoscopic images, though such images contain more detailed information and are subject to less noise and illumination effects. The simple input requirements and the robustness of its descriptors allow MED-NODE to be an effective tool within the diagnostic process for melanoma. In addition, the modular nature of the system allows for it to be easily extended. (C) 2015 Elsevier Ltd. All rights reserved.

机译：黑色素瘤是皮肤癌中最具侵略性的类型之一，在许多情况下，很难与良性痣区别开来。在这项贡献中，我们提出了一个决策支持（专家）系统，我们将其称为MED-NODE，它可以帮助医生完成这项艰巨的任务。所提出的系统利用了非皮下的病变数字图像，可以从中自动提取病变区域，然后计算有关颜色和纹理的描述符。另外，检查医师还提供了一组视觉属性。自动提取的描述符和医师提供的属性分别用于自动预测。最终分类是通过所有预测的多数票实现的。所提出的系统可实现较高的诊断准确性结果（81％），并且与使用皮肤镜图像的最新技术相媲美，尽管此类图像包含更详细的信息，并且受到的噪声和照明影响较小。简单的输入要求及其描述符的鲁棒性使MED-NODE成为黑色素瘤诊断过程中的有效工具。此外，系统的模块化特性使其易于扩展。（C）2015 Elsevier Ltd.保留所有权利。

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来源
《Expert Systems with Application》 |2015年第19期|6578-6585|共8页
作者
Giotis Ioannis; Molders Nynke; Land Sander; Biehl Michael; Jonkman Marcel F.; Petkov Nicolai;
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作者单位

Univ Groningen, Johann Bernoulli Inst Math & Comp, Groningen, Netherlands;

Univ Groningen, Univ Med Ctr Groningen, Ctr Blistering Dis, Dept Dermatol, Groningen, Netherlands;

Kings Coll London, Dept Biomed Engn, London WC2R 2LS, England;

Univ Groningen, Johann Bernoulli Inst Math & Comp, Groningen, Netherlands;

Univ Groningen, Univ Med Ctr Groningen, Ctr Blistering Dis, Dept Dermatol, Groningen, Netherlands;

Univ Groningen, Johann Bernoulli Inst Math & Comp, Groningen, Netherlands;

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正文语种 eng
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关键词
Computer-assisted diagnosis; Classification; Dermatology; Medical imaging; Melanoma;

机译：计算机辅助诊断;分类;皮肤科;医学影像学;黑色素瘤;

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专利

1. Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas. [J] . Zhao Liu, Jiuai Sun, Lyndon Smith, Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering . 2012,第5期

机译：皮肤镜检查图像上的分布定量，用于计算机辅助诊断皮肤黑色素瘤。
2. Computer-assisted melanoma diagnosis: a new integrated system [J] . Rubegni Pietro, Feci Luca, Nami Niccolo, Melanoma research . 2015,第6期

机译：计算机辅助黑色素瘤诊断：新的集成系统
3. Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases [J] . Cheng Chung-Chuan, Lu Chun-Feng, Hsieh Tsu-Yi, Journal of medical systems . 2015,第10期

机译：使用ANA IIF图像自动检测细胞类型以诊断自身免疫性疾病的计算机辅助系统的设计
4. SET OF DESCRIPTORS FOR SKIN CANCER DIAGNOSIS USING NON-DERMOSCOPIC COLOR IMAGES [C] . M. H. Jafari, S. Samavi, S. M. R. Soroushmehr, IEEE International Conference on Image Processing . 2016

机译：使用非Dermoscopic彩色图像的皮肤癌诊断的描述符
5. Investigation of image processing and computer-assisted diagnosis system for automatic video vision development assessment. [D] . Wang, Tsaipei. 2005

机译：用于自动视频视觉开发评估的图像处理和计算机辅助诊断系统的研究。
6. Scientific Demonstration Abstracts. Demonstration Abstracts: Computer-Assisted Medical Decision-Support Systems: Computer-Assisted Diagnosis and Teaching of Acid-Base Disorders [O] . Martin Goldberg 1990

机译：科学示范文摘。演示摘要：计算机辅助医疗决策支持系统：酸碱障碍的计算机辅助诊断和教学
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

机译：摘要的各个身体部位或器官可被分析以识别在人体内的不同的疾病。指甲分析来识别人体疾病的方法之一。指甲是身体一部分是离心脏最远，因此在最后接受氧气。作为结果，指甲是第一谁表现出人体疾病的症状。指甲可以容易地捕获用于诊断和没有重装或需要使用指甲图像用于疾病诊断，比如在其他测试和扫描过程没有特定的条件。人的指甲提供有关投诉或取决于它们的形状，纹理和色彩在人体内的任何营养失衡有益的信息。在人类中，许多全身性皮肤疾病是可以很容易地通过两个四肢指甲的仔细检查分析。很多指甲病已发现众多潜在系统性疾病的主要症状。在指甲的颜色，质地和形状的变化是许多疾病主要影响指甲的迹象。考虑到所有的指甲的这些性能的系统被提出，用于识别人指甲这样的变化以获得更精确的结果，并毫不费力预测许多疾病用途的数字图像处理（DIP）方法。随着物联网（IOT）的概念，新兴的互联网将生成的报告提供远程，这将帮助用户降低运输工作。由于系统必须处理大量的私人数据，数据的安全性必须得到保证。为了保持数据的机密性，使用Blockchain的概念，它是在数据管理领域的大多数新兴的概念之一。本文包含了数字图像处理的指甲图像，IOT（ThingSpeak云）的使用为数据存储和执行Blockchain的特征提取的执行，以保持固定的系统和盗窃免费。关键词：诠释薄GS（IOT），图像的ERNET PROC essin克，薄型gSpeak，RG乙vavalues，平均数PI XEL vavalues，阵营kchain，哈希密钥。疾病诊断系统：在人类指甲异常

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

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