基于人工神经网络(ANN)的乳房x线照片边缘检测算法设计

Alankrita Aggarwal, D. Chatha
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引用次数: 2

摘要

人工神经网络(ANN)用于解决与复杂场景和逻辑思维相关的问题。如今,令人担忧的是妇女因癌症导致的死亡率。一般来说,45岁左右的妇女最容易患这种疾病。早期发现是患者生存的唯一希望,否则可能会发展到无法恢复的阶段。目前,有许多技术可用于诊断这类疾病,其中乳房x光检查是检测早期癌症最值得信赖的方法。由于低对比度和不均匀背景,这些乳房x线照片的分析一直很困难。乳房x光图像被扫描,数字化处理,进一步降低感兴趣区域(ROI)和背景之间的对比度。此外,噪音、腺体和肌肉的存在导致背景对比度的变化。疑似肿瘤区域的边界往往是模糊和不恰当的。本文的目的是开发一种鲁棒的边缘检测技术,该技术在乳房x光片图像上工作最佳,以分割肿瘤区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)
An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.
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