Abstract
As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.
Highlights
In the clinical practice of reading and interpreting medical images, clinicians often refer to and compare the similar cases with verified diagnostic results in their decision making of detecting and diagnosing suspicious lesions or diseases
The relevant clinical information depicted on medical images is locally presented
Despite the fact that content-based image retrieval (CBIR) approach is still in its early development stage facing many technical challenges, as the digital medical images are produced in ever increasing quantities and used for diagnosis and therapy, the researchers believe that the advance of CBIR development will play more and more important role in future medical image diagnosis and patient treatment [11]
Summary
In the clinical practice of reading and interpreting medical images, clinicians (i.e., radiologists) often refer to and compare the similar cases with verified diagnostic results in their decision making of detecting and diagnosing suspicious lesions or diseases. The most of available search systems (or tools) developed and implemented in medical informatics and picture archiving and communication systems (PACS) use TBIR schemes that are based on the annotated textual information to select similar or clinically relevant references (cases) [1,2,3,4]. This approach is typically limited to retrieve or select the same type of medical images (i.e., mammograms or CT brain images). The discussed development and evaluation concepts should be applicable to CAD schemes for other types of medical images and diseases
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