A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images

Title

A Study of Segmentation Techniques to Detect Leukaemia in Microscopic Blood Smear Images

Creator

Patil A.P.

Description

In medical image processing, the segmentation of the image is considered to be a vital stage and is effectively used to extract the region of interest. Automated diagnosis of leukaemia is highly associated with the accurate segmentation of the cell nucleus. The purpose of this paper is to review and analyze literature related to some of the major segmentation techniques used in the field of Acute lymphoblastic leukaemia (ALL) detection. This paper presents an overview of segmentation methods along with the experimental results of six implemented methods and highlights some of the advantages and disadvantages of implemented segmentation techniques. 2020 IEEE.

Source

Proceedings of the 2020 IEEE International Conference on Communication, Computing and Industry 4.0, C2I4 2020

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Active contour; Acute lymphoblastic Leukemia; Artificial neural network; Fuzzy c-mean; Image segmentation; k-means; Region growing; Thresholding; watershed

Coverage

Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India, Department of Computer Application, CMR Institute of Technology, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-172818312-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/C2I451079.2020.9368928

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