Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …

Computer-aided diagnosis of masses in mammograms is important to the prevention of
breast cancer. Many approaches tackle this problem through content-based image retrieval …

Content-based image retrieval (CBIR) in large medical image archives is a challenging and
necessary task. Generally, different feature extraction methods are used to assign …

With the growing use of minimally invasive surgical procedures, endoscopic video archives
are growing at a rapid pace. Efficient access to relevant content in such huge multimedia …

Abstract Content-Based Image Retrieval (CBIR) systems have recently emerged as one of
the most promising and best image retrieval paradigms. To pacify the semantic gap …

Objective: We present a novel variant of the bag-of-visual-words (BoVW) method for
automated medical image classification. Methods: Our approach improves the BoVW model …

In traditional text based medical image retrieval system, it is hard to find visually similar
images in large medical image database. Content-based image retrieval is developed to …

Melanoma is the most fatal type of skin cancer. Detection of melanoma from dermoscopic
images in an early stage is critical for improving survival rates. Numerous image processing …

Content‐based medical image retrieval (CBMIR) systems attempt to search medical image
database to narrow the semantic gap in medical image analysis. The efficacy of high‐level …