Automatic design of W-operators using membership functions: a case study in brain MRI segmentation

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-11 DOI:10.1007/s12652-024-04789-9
Emilio José Robalino Trujillo, Agustina Bouchet, Virginia Laura Ballarin, Juan Ignacio Pastore
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Abstract

A W-operator is an image transformation that is locally defined inside a window W, invariant to translations. The automatic design of the W-operators consists of the design of functions, whose domain is a set of patterns or vectors obtained by translating a window through training images and the output of each vector is a class or label. The main difficulty to consider when designing W-operators is the generalization problem that occurs due to lack of training images. In this work, we propose the use of membership functions to solve the generalization problem in gray level images. Membership functions are defined from the training images to model regions that are often inaccurate due to ambiguous gray levels in the images. This proposal was applied to brain magnetic resonance image segmentation to test its performance in a field of interest in biomedical images. The experiments were carried out with different numbers of training and test images, windows sizes of \(3\times 3\), \(5\times 5\), \(7\times 7\), \(11\times 11\), and \(15\times 15\), and images with noise levels at 0, 1, 3, 5, 7, and 9\(\%\). To calculate the performance of each designed W-operator, the classification error, sensitivity, and specificity were used. From the experimental results, it was concluded that the best performance is achieved with a window of size \(3\times 3\). In images with noise levels from 1 to 5\(\%\), the classification error is less than 4\(\%\) and the sensitivity and specificity are greater than 94 and 98\(\%\), respectively.

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使用成员函数自动设计 W 运算器:脑磁共振成像分割案例研究
W 运算符是在 W 窗口内局部定义的图像变换,对平移不变。W 运算符的自动设计包括函数的设计,其域是通过训练图像平移窗口获得的一组模式或向量,每个向量的输出是一个类别或标签。设计 W 运算符时需要考虑的主要困难是由于缺乏训练图像而产生的泛化问题。在这项工作中,我们建议使用成员函数来解决灰度图像中的泛化问题。成员函数是根据训练图像定义的,用于对由于图像中模糊的灰度级而经常不准确的区域进行建模。我们将这一建议应用于脑磁共振图像分割,以测试其在生物医学图像领域的性能。实验使用了不同数量的训练图像和测试图像,窗口大小分别为(3乘以3)、(5乘以5)、(7乘以7)、(11乘以11)和(15乘以15),图像的噪声水平分别为0、1、3、5、7和9(%)。为了计算所设计的 W 操作符的性能,使用了分类误差、灵敏度和特异性。从实验结果中可以得出结论,使用大小为 (3\times 3\ )的窗口可以获得最佳性能。在噪声水平为1到5的图像中,分类误差小于4,灵敏度和特异性分别大于94和98。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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