Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

Abstract

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and nearinfrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu's method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Principles of Hyperspectral Microscope Imaging Techniques and Their Applications in Food Quality and Safety Detection: A Review

“…Visible/near-infrared hyperspectral microscope imaging. One of the advantages in using Vis/NIR HMI is its high resolution, allowing it to be used in medical science such as counting red blood cells (Liu, Zhou et al, 2017). The high resolution also makes it suitable for detecting microorganisms.…”

Classification of Microscopic Hyperspectral Images of Blood Cells Based on Lightweight Convolutional Neural Network

“…Microscopic hyperspectral imaging technology has been applied in various aspects of the medical field [5,6], including detecting tumors in the human body [7][8][9], distinguishing normal tissues from cancerous tissues [10][11][12], and facilitating the cytological diagnosis of cancer cells [13][14][15], as well as assisting in the diagnosis and characterization of the central nervous system [16][17][18]. These studies have demonstrated that tissues with similar biochemical compositions are more likely to exhibit similar spectral features.…”

Deep learning-based label-free hematology analysis framework using optical diffraction tomography

“…To solve this problem, various methods using label-free imaging modalities in hematology analysis have been studied. For instance, Raman microscopy [16] , [17] , [18] , hyperspectral imaging [19] , [20] , [21] , and defocusing phase-contrast imaging [22] , [23] have been exploited for hematology analysis. These methods use the endogenous imaging contrast of samples themselves without using any staining agent, but require complex optical systems and long data acquisition times.…”