Classification and automatic segmentation for Detection Leukemia

Jagannath Jadhav

ISBN 10: 6202814829 / ISBN 13: 9786202814829

Published by LAP LAMBERT Academic Publishing Sep 2020, 2020

About this Item

Neuware -The proposed work is to present a methodology for segmenting the nuclei of white blood cells based on Gram Schmidt orthogonalization & Sparse Representation for white blood cells classification. The differential counting of white blood cells reveals invaluable information to hematologist. These informations are very useful to hematologist for diagnosis and treatment of many diseases. The nucleus of white blood cells has the most information about type of white blood cells, thus an accurate segmentation of white blood cell¿s nucleus seems to be helpful for other stages of automatic recognition of white blood cells. The system will focus on features of white blood cells to detect, Leukemia disease.The system will use features of microscopic images & examine changes on texture, geometry, color, statistical. Changes in these features will be used as a classifier input.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch.

Seller Inventory # 9786202814829

Bibliographic Details

• Title: Classification and automatic segmentation for Detection Leukemia
• Publisher: LAP LAMBERT Academic Publishing Sep 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 6202814829
• ISBN 13: 9786202814829
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The proposed work is to present a methodology for segmenting the nuclei of white blood cells based on Gram Schmidt orthogonalization & Sparse Representation for white blood cells classification. The differential counting of white blood cells reveals invaluable information to hematologist. These informations are very useful to hematologist for diagnosis and treatment of many diseases. The nucleus of white blood cells has the most information about type of white blood cells, thus an accurate segmentation of white blood cell’s nucleus seems to be helpful for other stages of automatic recognition of white blood cells. The system will focus on features of white blood cells to detect, Leukemia disease.The system will use features of microscopic images & examine changes on texture, geometry, color, statistical. Changes in these features will be used as a classifier input.