Pixel-Classification-Based Reticulocyte Detection in Blood-Smear Microscopy Images

Abstract

Abstract Methods for reticulocyte identification and counting based on digital image processing technology are rare. In this paper, we proposed a pixel-based reticulocyte identification method for blood micrographs. This approach not only addresses slowness of manual methods but also largely alleviates the susceptibility of flow-based methods to nucleic acid. The key is to extract pixel-level features. One color feature is extracted from three color spaces, namely RGB, HSI, and LUV. Three texture features called Gabor features, the gray-level co-occurrence matrix, and local contrast pattern are extracted from the Cr channel of the YCbCr color space, forming six features. Subsequently, the recognition effects of each combination of color and texture were compared, and the combination of Gabor texture features and LUV color features was selected. Then, a support vector machine (SVM) classifier was used to classify the pixel-level features, and the RNA-staining area was detected. Based on the location, number, and area of the region, whether the target cell is reticulocyte can be determined. The precision of this method for reticulocytes was 98.4%, recall was 98.0%, and the F1 measure was 0.982, indicating its usefulness for automation equipment.