Medical imaging is fundamental to modern healthcare, and its widespread use has resulted
in the creation of image databases, as well as picture archiving and communication systems …

With the ever-increasing requirement of storage and computation resources, it is unrealistic
for local devices (with limited sources) to implement large-scale data processing. Therefore …

Deep learning is quickly becoming the leading methodology for medical image analysis.
Given a large medical archive, where each image is associated with a diagnosis, efficient …

Content-based image retrieval (CBIR) techniques have currently gained increasing
popularity in the medical field because they can use numerous and valuable archived …

A complete prototype for the automatic detection of normal examinations on a
teleophthalmology network for diabetic retinopathy screening is presented. The system …

Content-based medical image retrieval (CBMIR) is one of the most challenging and
ambiguous tasks used to minimize the semantic gap between images and human queries in …

In this paper, a new image indexing and retrieval algorithm using local mesh patterns are
proposed for biomedical image retrieval application. The standard local binary pattern …

A new image feature description based on the local wavelet pattern (LWP) is proposed in
this paper to characterize the medical computer tomography (CT) images for content-based …

This paper presents a novel feature extraction algorithm called local ternary co-occurrence
patterns (LTCoP) for biomedical image retrieval. The LTCoP encodes the co-occurrence of …

A novel image feature descriptor based on the local bit-plane decoded pattern (LBDP) is
introduced for indexing and retrieval of biomedical images in this paper. A local bit-plane …