Extended Query Refinement for Medical Image Retrieval
نویسندگان
چکیده
منابع مشابه
Query Refinement and User Relevance Feedback for Contextualized Image Retrieval
The motivation of this paper is to increase the user perceived precision of results of Content Based Information Retrieval (CBIR) systems with Query Refinement (QR), Visual Analysis (VA) and Relevance Feedback (RF) algorithms. The proposed algorithms were implemented as modules into K-Space CBIR system. The QR module discovers hypernyms for the given query from a free text corpus (Wikipedia) an...
متن کاملExtended Query Refinement for Content-Based Access to Large Medical Image Databases
The differentiating characteristics of text versus images and their impact on large medical image databases intended to allow content-based indexing and retrieval have recently been explored. For the design of powerful user interfaces, we propose a grouping of the various mechanisms into four classes: (i) output modules, (ii) parameter modules, (iii) transaction modules, and (iv) process module...
متن کاملA Radon-based Convolutional Neural Network for Medical Image Retrieval
Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...
متن کاملManual Query Modification and Data Fusion for Medical Image Retrieval
Image retrieval has great potential for a variety of tasks in medicine but is currently underdeveloped. For the ImageCLEF 2005 medical task, we used a text retrieval system as the foundation of our experiments to assess retrieval of images from the test collection. We conducted experiments using automatic queries, manual queries, and manual queries augmented with results from visual queries. Th...
متن کاملQDFA: Query-Dependent Feature Aggregation for Medical Image Retrieval
We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods. key words: C...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Digital Imaging
سال: 2007
ISSN: 0897-1889,1618-727X
DOI: 10.1007/s10278-007-9037-4