Semantic-Based Brain MRI Image Segmentation Using Convolutional Neural Network

Chou, Yao; Lee, Dah Jye; Zhang, Dong

doi:10.1007/978-3-319-50835-1_56

Cited by 1 publication

(1 citation statement)

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“…There were many systems of semantic-based image retrieval, which have been published and applied in a variety of fields such as a semantic framework image retrieval based on highlevel semantics and image annotations applied on CT images [5], a semantic-based medical imaging retrieval using Convolutional Neural Network (CNN) for brain MRI image [30], a semantic-based application in the distributed information systems [9], a medical case-based image retrieval based on textual and image information in RadLex ontology [2], etc. In each of the different areas, multimedia systems need to be extracted the semantic of objects to describe content.…”

Section: Introductionmentioning

confidence: 99%

Sir-Dl: An Architecture of Semantic-Based Image Retrieval Using Deep Learning Technique and RDF Triple Language

Van

Khoi

et al. 2019

JCC

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The problem of finding and identifying semantics of images is applied in multimedia applications of many different fields such as Hospital Information System, Geographic Information System, Digital Library System, etc. In this paper, we propose the semantic-based image retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents. At the same time we identify the semantics of similar images on Ontology, which describes semantics of visual features of images. Firstly, the color and spatial features of segmented images are we extracted and these visual feature vectors are trained on the deep neural network to obtain visual words vectors. The process of image retrieval is executed rely on semantic classification of SIR-DL according to the visual feature vector of the query image from which it produces a visual word vector. Then, we retrieve it on Ontology to provide the identities and the semantics of similar images corresponds to a similarity measure. In order to carry out SIR-DL, the algorithms and diagram of this image retrieval system are proposed after that we implement them on ImageCLEF@IAPR, which has 20,000 images. On the base of the experimental results, the effectiveness of our method is evaluated by the accuracy, precision, recall, and F-measure; these results are compared with some of works recently published on the same image dataset. It shows that SIR-DL effectively solves the problem of semantic-based image retrieval and can be used to build multimedia systems in many different fields.

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Section: Introductionmentioning

confidence: 99%