Open AccessSpectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology
Author(s) -
Qian Wang,
Jianbiao Wang,
Mei Zhou,
Qingli Li,
Yi-Ting Wang
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.003017
Subject(s) - hyperspectral imaging , artificial intelligence , pattern recognition (psychology) , computer science , feature extraction , support vector machine , normalization (sociology) , artificial neural network , learning vector quantization , rgb color model , feature vector , feature (linguistics) , hsl and hsv , computer vision , medicine , linguistics , philosophy , virus , virology , sociology , anthropology
Microscopic examination is one of the most common methods for acute lymphoblastic leukemia (ALL) diagnosis. Most traditional methods of automized blood cell identification are based on RGB color or gray images captured by light microscopes. This paper presents an identification method combining both spectral and spatial features to identify lymphoblasts from lymphocytes in hyperspectral images. Normalization and encoding method is applied for spectral feature extraction and the support vector machine-recursive feature elimination (SVM-RFE) algorithm is presented for spatial feature determination. A marker-based learning vector quantization (MLVQ) neural network is proposed to perform identification with the integrated features. Experimental results show that this algorithm yields identification accuracy, sensitivity, and specificity of 92.9%, 93.3%, and 92.5%, respectively. Hyperspectral microscopic blood imaging combined with neural network identification technique has the potential to provide a feasible tool for ALL pre-diagnosis.