一种新的特征袋加权二维矢量量化编码方法用于组织病理图像分类

Q3 Computer Science
Raju Pal, M. Saraswat
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引用次数: 6

摘要

由于组织病理图像复杂的形态结构,自动组织病理图像分析是一个具有挑战性的问题。特征袋表示是一种突出的图像表示方法,已成功应用于组织病理学图像分析。特征袋方法分为特征提取、码本构建、特征编码和分类四个阶段。其中特征编码是主要阶段之一。在特征编码阶段,图像在输入到支持向量机分类器之前,先用视觉词表示。然而,特征袋框架的特征编码阶段考虑用视觉词来编码每个图像的一个特征,因此系统无法利用其他特征的优点。因此,为了提高特征袋框架的有效性,本文提出了一种新的加权二维矢量量化编码方法。在两个组织病理图像数据集上进行了分类测试。实验结果表明,SIFT和ORB特征结合二维矢量量化编码方法在ADL和Blue组织学数据集上的准确率分别为80.13%和77.13%,优于其他考虑的编码方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification
Automated histopathological image analysis is a challenging problem due to the complex morphological structure of histopathology images. Bag-of-features is one of the prominent image representation methods which has been successfully applied in histopathological image analysis. There are four phases in the bag-of-features method, namely feature extraction, codebook construction, feature encoding, and classification. Out of which feature encoding is one of the prime phases. In feature encoding phase, images are represented in terms of visual words before feeding into support vector machine classifier. However, the feature encoding phase of the bag-of-features framework considers the one feature to encode each image in terms of visual words due to which the system can not use the merits of other features. Therefore, to improve the efficacy of the bag-of-features framework, a new weighted two-dimensional vector quantisation encoding method is proposed in this work. The proposed method is tested on two histopathological image datasets for classification. The experimental results show that the combination of SIFT and ORB features with two dimensional vector quantisation encoding method returns 80.13% and 77.13% accuracy on ADL and Blue Histology datasets respectively which is better than other considered encoding methods.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
21
期刊介绍: Intelligent information systems and intelligent database systems are a very dynamically developing field in computer sciences. IJIIDS provides a medium for exchanging scientific research and technological achievements accomplished by the international community. It focuses on research in applications of advanced intelligent technologies for data storing and processing in a wide-ranging context. The issues addressed by IJIIDS involve solutions of real-life problems, in which it is necessary to apply intelligent technologies for achieving effective results. The emphasis of the reported work is on new and original research and technological developments rather than reports on the application of existing technology to different sets of data.
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