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An automated content-based segmentation framework: Application to MR images of knee for osteoarthritis research

机译：一个基于内容的自动分割框架：应用于膝关节MR图像的骨关节炎研究

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摘要

To effectively diagnose and monitor the treatment of diseases such as osteoarthritis, the segmentation, processing and analysis of mass volumes of medical images is gaining high importance. In this paper, a new fully automated content-based segmentation framework is proposed. The framework is designed to be compatible with a wide variety of segmentation techniques. To this end, a novel content-based two-pass block discovery mechanism is proposed to provide full automation for image segmentation. The proposed framework uses both training and local image data and disjoint block-wise image scanning to achieve ROI and background block discovery. The detected object and background blocks are then used to initialize and support the segmentation process. The effectiveness of the proposed framework is demonstrated by performing automatic segmentation of the femur and tibia bones in knee osteoarthritis MR images with 96% accuracy. Experimental results are provided which show the effectiveness of the proposed framework.
机译：为了有效地诊断和监测诸如骨关节炎的疾病的治疗，医学图像的质量的分割，处理和分析变得越来越重要。在本文中，提出了一种新的基于内容的完全自动化的分割框架。该框架旨在与多种细分技术兼容。为此，提出了一种新颖的基于内容的两遍块发现机制，以提供用于图像分割的完全自动化。所提出的框架使用训练和本地图像数据以及不相交的逐块图像扫描来实现ROI和背景块发现。然后，将检测到的对象块和背景块用于初始化和支持分割过程。通过以96％的准确度对膝骨关节炎MR图像进行股骨和胫骨的自动分割，证明了所提出框架的有效性。提供的实验结果表明了所提出框架的有效性。

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