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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >Intelligent breast tumor detection system with texture and contrast features
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Intelligent breast tumor detection system with texture and contrast features

机译：具有纹理和对比度特征的智能乳腺肿瘤检测系统

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摘要

According to a research report by the World Health Organization (WHO), breast cancer is the most common type of cancer in women, while the mortality rate of breast cancer of females over 40 years old is extremely high. If detected early, it can be treated early, and the mortality rate of breast cancer can be reduced. Meanwhile, the image processing and pattern recognition technology has been adopted to select suspicious regions, provides alerts to assist in doctors' diagnosis, and reduces misdiagnosis rates due to fatigue of doctors, and improves diagnostic accuracy. Hence, this paper proposed an intelligent breast tumor detection system with texture and contrast features. This system consists of three parts: preprocessing, feature extraction, and learning algorithm. The goal of preprocessing is to obtain a good image quality and a real breast area. In the feature extraction, we extract the two features to describe the breast tumor. These features include Laws' Mask features which are the representation of the texture and modification average distance (MAD) feature which is the representation of the contrast. Each region of interest (ROI) image block will be extracted by these two features. And we will extract useful feature from all extracted features. We hope that a small quantity of feature can be used in our proposed system. Next, we use neural network as learning algorithm to detect the tumor with extracted features. Finally, in the experimental results, we use three databases to verify our proposed system, and two radiologists participated in that process and designed final verification study. Thus, we understand from the experimental results that a detection rate as high as 98% can be achieved by using only a few features and the simplest artificial neural network rather than a large number of features and a complex classifier. The success of the system will improve the accuracy of the existing detection methods, assist medical diagnosis, and decrease the time of the judgment effective by doctors.
机译：根据世界卫生组织（WHO）的研究报告，乳腺癌是女性最常见的癌症类型，而40岁以上女性的乳腺癌死亡率非常高。如果及早发现，可以及早治疗，并且可以降低乳腺癌的死亡率。同时，采用图像处理和模式识别技术来选择可疑区域，提供警报以帮助医生诊断，并减少由于医生疲劳而引起的误诊率，并提高诊断准确性。因此，本文提出了一种具有纹理和对比度特征的智能乳腺肿瘤检测系统。该系统由三部分组成：预处理，特征提取和学习算法。预处理的目的是获得良好的图像质量和真实的乳房区域。在特征提取中，我们提取两个特征来描述乳腺肿瘤。这些功能包括代表纹理的Laws遮罩功能和代表对比度的修改平均距离（MAD）功能。这两个特征将提取每个感兴趣区域（ROI）图像块。我们将从所有提取的特征中提取有用的特征。我们希望可以在我们建议的系统中使用少量功能。接下来，我们使用神经网络作为学习算法来检测具有提取特征的肿瘤。最后，在实验结果中，我们使用三个数据库来验证我们提出的系统，并且两名放射科医生参与了该过程并设计了最终验证研究。因此，我们从实验结果中了解到，仅使用少量特征和最简单的人工神经网络，而不使用大量特征和复杂的分类器，可以实现高达98％的检测率。该系统的成功将提高现有检测方法的准确性，有助于医学诊断，并减少医生做出有效判断的时间。

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