Advances in medical digital imaging have greatly benefited patient care. Computer-aided diagnosis is increasingly being used to facilitate semior fullyautomatic medical image analysis and image retrieval. While different tasks involve different methodologies in this domain, these tasks normally require image feature extraction as an essential component in the algorithmic framework. In this Chap...

The main problem in content–based medical image retrieval system is semantic gap, where the meaning that the user has in mind for an image is at a higher semantic level than the features on which the database operates. To overcome the shortcomings in the current content–based medical image retrieval systems and to provide a mechanism to better understand the images semantics, a fuzzy rule based...

Several approaches are proposed for retrieving images. Each of them describes image according to application domain requirements. No global approach exists to resolve retrieving image in complex domains (as medical one), in which content is multifaceted. A framework to retrieve medical images is presented. In this paper, we expose our three-dimensional approach applied to medical domain, and re...

In this paper, a Content Based Image Retrieval (CBIR) framework for medical images has been presented. An algorithm based on energy information obtained from Hilbert Transform for extracting features from medical images based on imaging modalities. The features are selected on the basis of the correlation among the extracted vectors depending upon the class label. An enhanced Genetic Algorithm ...

Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has the potential to help better managing the rising amount of visual medical data. One of the most frequent application fields for content– based medical image retrieval (CBIR) is diagnostic aid. By submitting an image showing a certain pathology to a CBIR syste...

The ever increasing number of medical images in hospitals urges on the need for generic content based image retrieval systems. These systems are in an area of great importance to the healthcare providers. The first and foremost function in such system is feature extraction. In this paper, different feature extraction techniques have been utilized to represent medical blood cell images. They are...

Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is there...

Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture fe...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...