Using texture-based symbolic features for medical image representation

Abstract

At present time the Internet has become a major source of information and a powerful didactic tool. Furthermore, the development of digital equipment, allows to acquire and store large quantities of medical data, including images. In the context of the CISMeF on-line health-catalogue, our work is centered on the automatic categorization of medical images according to their visual content, for further indexation and retrieval tasks. The aim of the present study is to assess the performance of a new image symbolic descriptor for medical modality, anatomic region and view angle image categorization. This descriptor is issued from the unsupervised partition of statistical and texture image subblock representations. A medical image database of 10322 images from 33 classes was ground-truthed by a domain expert. Despite the complexity and variability of medical images, the compact symbolic representation approach proposed in this paper achieves high recognition rates. Thus, using kNN classifiers, we obtain an average precision of 83% and a top performance of 91.19%.

An efficient radiographic Image Retrieval system using Convolutional Neural Network

“…This is probably due to the use of high-level, data-driven CNN-descriptors in the first stage, while the competitors rely mainly on hand-crafted mid-or low-level features. [20] 73.21% Chowdhury at al., 2012 [21] 76.00% Collins et al, 2013 [22] 70.12% Ayyachamy et al, 2013 [23] 60.16% Camlica et al, 2015 [24] 82.01% Proposed Approach 97.79%…”

“…In [20], the authors used 768 features for image representation and a K-NN classifier for classification. Here, each image is split in 16 equal sub-blocks and each sub-block is described using 48 features.…”

“…We implemented in Matlab the methods proposed in [20], [21], [22], [23], [24] and applied them to the same dataset. As shown, the proposed method performs significantly better than the competitors.…”

Categorization Method Research for Medical Image Using Gaussian Mixture Model

“…The quality of the classification is due to the choice of the features and the categorization algorithm. Many experts have presented lots of methods to represent the images using features comprising intensity, shape and texture [2] [8]. These features such as intensity histogram, gray-level co-occurrence matrix, Fourier transform are used widely in papers recently.…”