Image Analysis in Big Data Architecture using Artificial Intelligence: Compression and Analysis of Biomedical Image Based on Machine Learning and Orthogonal Transforms with Application

Tchinda, Rene/ Tchagna Kouanou, Aurelle/ TCHIOTSOP, Daniel

ISBN 10: 6200480400 / ISBN 13: 9786200480408

Verlag: LAP LAMBERT Academic Publishing, 2019

Beschreibung

148 pages. 8.66x5.91x0.34 inches. In Stock.

Bestandsnummer des Verkäufers zk6200480400

Bibliografische Details

• Titel: Image Analysis in Big Data Architecture using Artificial Intelligence: Compression and Analysis of Biomedical Image Based on Machine Learning and Orthogonal Transforms with Application
• Verlag: LAP LAMBERT Academic Publishing
• Veröffentlichungsdatum: 2019
• Sprache: Englisch
• ISBN-10: 6200480400
• ISBN-13: 9786200480408
• Bindung: Paperback
• Zustand: Brand New

Über diesen Titel

Inhaltsangabe

In the medical field, data is increasingly growing and traditional methods cannot manage them efficiently. In the computational biomedical, the continuous challenges are management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of the data using machine learning and artificial intelligence techniques. It becomes very important to develop methods and/or architectures based on big data technologies for complete processing of biomedical images data. In this thesis, we propose a complete and optimal workflow based on big data technology and optimal algorithms drawn from literature to manage biomedical images. Compression step within the proposed optimal workflow will be considered as a study case implementing big data analysis technology. The proposed workflow implements an image compression algorithm for biomedical images, which is based on three main steps, orthogonal transform, vector quantization using machine learning and entropy encoding. The proposed algorithm allows us to develop appropriate and efficient methods to leverage a large number of images into the proposed workflow.