The availability of medical imaging data from clinical archives, research literature, and
clinical manuals, coupled with recent advances in computer vision offer the opportunity for …

Medical-image-based diagnosis is a tedious task ‚and small lesions in various medical
images can be overlooked by medical experts due to the limited attention span of the human …

Rapid advancement in imaging technology generates an enormous amount of
heterogeneous medical data for disease diagnosis and rehabilitation process. Radiologists …

In this paper, a simple feature fusion scheme is proposed for content-based image retrieval
(CBIR) using color, texture and shape feature moments. The low dimensional feature …

Medical image fusion and classification has been utilized to get valuable significant data
multimodality medical image data. The fundamental commitment of this paper is to extricate …

Nearest neighbor search, also known as NNS, is a technique used to locate the points in a
high-dimensional space closest to a given query point. This technique has multiple …

This paper presents a unified learning framework for heterogeneous medical image retrieval
based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA) …

Abstract Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in
retrieving medical images with textual descriptions. Usually, these descriptions are very brief …

In this paper we propose a complete pipeline for medical image modality classification
focused on the application of discrete Bayesian network classifiers. Modality refers to the …

In this paper we depict an implemented system for medical image retrieval. Our system
performs retrieval based on both textual and visual content, separately and combined, using …