基于聚类算法的肝活检图像胶原比例面积自动提取

Dimosthenis C. Tsouros, Panagiotis N. Smyrlis, M. Tsipouras, D. Tsalikakis, N. Giannakeas, A. Tzallas, P. Manousou
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引用次数: 8

摘要

肝脏疾病的诊断和分期对药物治疗效果和治疗策略至关重要。肝组织活检中胶原比例面积(CPA)的测定已成为评估肝组织纤维化的有效工具。采用最先进的图像处理技术来分析活检图像,提供对疾病严重程度的客观评估。在目前的工作中,提出了一种新的k均值聚类方法用于肝活检图像分割。更具体地说,利用了质心运动的监督限制。在第一阶段,使用图像训练集为每个类提取一个超立方体。然后,在每个超立方体内初始化一个质心,并且在集群的迭代期间只允许在超立方体内移动。为了评估所提出的方法,使用了8个肝活检图像,并计算了每个图像的分类结果以及CPA值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Collagen Proportional Area Extraction in Liver Biopsy Images Using a Novel Classification via Clustering Algorithm
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized. In the first stage, a training set of images are employed to extract a hypercube for each class. Then, one centroid is initialized inside each hypercube and during the iterations of the clustering is allowed to move only inside the hypercube. For the evaluation of the proposed method 8 liver biopsy images are employed and classification results along with CPA values are computed for each image.
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