Rahman Md Mahmudur, Bhattacharya Prabir, Desai Bipin C
Department of Computer Science Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
IEEE Trans Inf Technol Biomed. 2007 Jan;11(1):58-69. doi: 10.1109/titb.2006.884364.
A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.
本文提出了一种基于内容的图像检索（CBIR）框架，用于不同成像模态、具有不同方向的解剖区域和生物系统的医学图像的多样化集合。在这样的数据库（DB）中，图像的组织通过预定义的语义类别进行了明确的定义；因此，它对于特定类别的搜索可能是有用的。所提出的框架由用于图像预过滤的机器学习方法、使用统计距离度量的相似性匹配以及相关反馈（RF）方案组成。为了缩小语义鸿沟并提高检索效率，我们研究了监督学习和无监督学习技术，以便在基于主成分分析（PCA）的特征子空间中，将低级全局图像特征（例如颜色、纹理和边缘）与其高级语义和视觉类别相关联。特别地，我们探索使用概率多类支持向量机（SVM）和模糊c均值（FCM）聚类对图像进行分类和预过滤，以减少搜索空间。在预过滤后的图像上，在更精细的层面上提出了特定类别的统计相似性匹配。为了纳入更好的感知主观性，还添加了一种RF机制，以动态更新查询参数并调整所提出的匹配函数。实验基于一个由20个预定义类别的5000张不同医学图像组成的真实数据库。报告了基于交叉验证（CV）准确性以及图像分类和检索的精确率-召回率的结果分析。它证明了所提出框架所实现的改进、有效性和效率。
