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首页> 外文期刊>American journal of applied sciences >AN INTEGRATED FRAMEWORK BASED ON TEXTURE FEATURES, CUCKOO SEARCH AND RELEVANCE VECTOR MACHINE FOR MEDICAL IMAGE RETRIEVAL SYSTEM
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AN INTEGRATED FRAMEWORK BASED ON TEXTURE FEATURES, CUCKOO SEARCH AND RELEVANCE VECTOR MACHINE FOR MEDICAL IMAGE RETRIEVAL SYSTEM

机译：基于纹理特征，布谷鸟搜索和相关矢量机的医学图像检索系统集成框架

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摘要

As medical images are widely used in healthcare applications, Content Based Medical Image Retrieval (CBMIR) system is needed for physicians to convey effective decisions to patients and for medical research students to learn imaging characteristics for their extensive research based on visual features. However the performance of the retrieval is restricted due to high feature dimensionality of visual features. To reduce the high feature dimension, an integrated approach is proposed such as Visual feature extraction, Feature selection, Feature Classification and Similarity measurements. The selected feature is texture features by using Local Binary Patterns (LBP) in which extracted texture features are designed as feature vector database. Fuzzy based Cuckoo Search (FCKS) techniques are applied for feature selection to reduce the high feature vector dimensionality and addresses the difficulty of feature vectors being surrounded in local feature optima also the global optimum feature position to be special for all feature cuckoo hosts. Fuzzy based Relevance Vector Machine (FRVM) classification is an proficient method to customize the collections of relevant image features that would classify dimensionally determined optimized feature vectors of images. The Euclidean Distance (ED) is a standard technique for similarity measurement between the query image and the image database. The proposed system is implemented on thousands of medical images and achieved a high retrieval precision and recall compared with other two methods as validated through experiments.
机译：由于医学图像已广泛用于医疗保健应用中，因此需要基于内容的医学图像检索（CBMIR）系统，以便医师向患者传达有效的决定，并使医学研究学生学习基于视觉特征的广泛研究的成像特征。然而，由于视觉特征的高特征尺寸，检索的性能受到限制。为了减小高维特征，提出了一种集成方法，例如视觉特征提取，特征选择，特征分类和相似度测量。所选特征是通过使用局部二进制图案（LBP）的纹理特征，其中提取的纹理特征被设计为特征向量数据库。基于模糊的杜鹃搜索（FCKS）技术用于特征选择，以降低高特征向量维数，并解决了局部局部最优中包围特征向量的难题，以及针对所有杜鹃宿主特有的全局最佳特征位置。基于模糊的关联向量机（FRVM）分类是一种自定义相关图像特征集合的有效方法，该集合将对尺寸确定的图像优化特征向量进行分类。欧氏距离（ED）是用于查询图像与图像数据库之间相似度测量的标准技术。与通过实验验证的其他两种方法相比，该系统可在数千张医学图像上实现，并具有较高的检索精度和召回率。

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