International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org

Call for Paper, Published Articles, Indexing Infromation Comparative Study of Content Based Image Retrieval using Segmentation Techniques for Brain Tumor Detection from MRI Images, Authors : Ms.sheetal Ashokrao wadhai , Dr. Seema S. Kawathekar., International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), www.ijettcs.org
Volume & Issue no: Volume 10, Issue 4, July - August 2021

Title:
Comparative Study of Content Based Image Retrieval using Segmentation Techniques for Brain Tumor Detection from MRI Images
Author Name:
Ms.sheetal Ashokrao wadhai , Dr. Seema S. Kawathekar.
Abstract:
Abstract: In this discussion the need for alternate access approaches of medical information processing against the already dominant text-based methods. The vast volume of visual data generated, the growing diversity of medical imaging data, and evolving usage habits all contribute to this need. Significant volumes of unused information are contained in the visual data, which can be used to aid diagnosis, training, and testing if properly used. Before addressing technology introduced in the medical sector, the chapter briefly discusses the history of image retrieval and its general processes. We will go into how to assess medical content-based image retrieval (CBIR) technologies, as well as their capabilities, drawbacks, and future innovations. The Med GIFT project and the IRMA (Image Retrieval in Medical Applications) platform are used as examples. Keywords: Content Based Image Retrieval (CBIR), Shape, Feature Extraction, Segmentation, color, Texture
Cite this article:
Ms.sheetal Ashokrao wadhai , Dr. Seema S. Kawathekar. , " Comparative Study of Content Based Image Retrieval using Segmentation Techniques for Brain Tumor Detection from MRI Images " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 10, Issue 4, July - August 2021 , pp. 001-010 , ISSN 2278-6856.
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