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Biomed Opt Express. 2017 May 19;8(6):3017-3028. doi: 10.1364/BOE.8.003017. eCollection 2017 Jun 1.

Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology.

Author information

1
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China.
2
Ruijin Hospital, Shanghai 200025, China.

Abstract

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.

KEYWORDS:

(100.0100) Image processing; (100.4145) Motion, hyperspectral image processing; (110.0110) Imaging systems; (110.2960) Image analysis

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