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A study of the phases of classification of liver diseases from ultrasound images and gray level difference weights based segmentation

机译:基于超声图像和基于灰度差权重的分割对肝脏疾病分类的阶段研究

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This paper presents a study of the state of the art techniques applied to computer based analysis and classification of liver diseases from ultrasound images. The diseased portions from the ultrasound images are analyzed and categorized using techniques such as Despeckling, Segmentation, Feature extraction and Classification. Automatic segmentation of ultrasound images is complicated due to the fact that the image may include other organs which are close to the liver, irregular structure of disease, poor quality of image, lack of color cues, and lack of definite boundaries and presence of noise. This work makes a study of different techniques used in the different phases of biomedical liver ultrasound processing such as noise removal, segmentation, Feature Extraction and classification. This work also presents the segmentation results obtained using Gray Level Difference Weights Method on 10 types of liver diseases from ultrasound images.
机译:本文介绍了用于基于计算机的超声图像肝病分析和分类的最先进技术的研究。使用诸如去斑点,分割,特征提取和分类之类的技术来分析和分类来自超声图像的患病部分。由于图像可能包括靠近肝脏的其他器官,疾病的不规则结构,图像质量差,缺少颜色提示以及缺乏明确的边界和噪声,因此超声图像的自动分割非常复杂。这项工作研究了在生物医学肝脏超声处理的不同阶段中使用的不同技术,例如噪声去除,分割,特征提取和分类。这项工作还提出了使用灰度差异权重法从超声图像中对10种肝病进行分割的结果。

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