Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2727
Title: GLRLM based Feature Extraction for Acute Lymphoblastic Leukemia(ALL) Detection
Authors: Mishra, Sonali
Majhi, Banshidhar
Sa, Pankaj Kumar
Siddiqui, Faiza
Keywords: Acute Lymphoblastic Leukemia
Grey level run length
Marker-based watershed segmentation
CAD system
Issue Date: Jun-2017
Publisher: Springer
Citation: 5th International Conference on Advanced Computing, Networking, and Informatics(ICACNI), NIT Goa, India,1-3 June 2017
Abstract: This paper proposes a gray level run length matrix (GLRLM) based feature extraction technique for the detection of Acute Lymphoblas-tic Leukemia (ALL). ALL could be a fatal hematopoietic ailment which might cause death if it's not treated at the early stage. The GLRL matrix is a method for extraction of statistical textural features from the nucleus of the lymphocyte image. The extracted features are then supplied to the Support Vector Machine (SVM) for classi_cation. The experiments are performed on an publicly available dataset ALL-IDB1. The accuracy of the proposed scheme is found to be 96.97% for SVM classifier
Description: Copyright for this paper belongs to proceeding publisher
URI: http://hdl.handle.net/2080/2727
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2017_ICACNI_SMishra_GLRLM.pdf554 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.