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

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.

• Publication year

  2017

• Venue

  Biomedical Optics Express

• Publication date

  2017-06-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1364/BOE.8.003017PMID 28663923PMCID PMC5480446
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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