Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images.

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Automated classification of white blood cells from microscope images is still challenging, particularly in terms of feature representations choice considering its complexity, compactness and efficiency. Particularly, in this scenario, the feature representations have to be invariant to non-uniform illumination, shape of the nuclei, stage of maturity, change in topology due to rotation, scale and shifting. This paper proposes a new white blood cell feature representation which aims at increasing robustness to those challenges. The proposed feature representation is designed based on L-moments (L-skewness, L-mean, L-scale and L-kurtosis) of the Radon projection of segmented nuclei shape. Coupled with Linear Discriminant Analysis, the proposed feature representation has been shown to be highly effective at encoding the discriminative properties of the white blood cells, and invariant to intra-class cell variations. Support Vector Machine (SVM) based (ones-vs-all) schema and a classification tree are applied to separate the multiple classes of cells. The proposed approach is evaluated for a 10-class problem. It achieves an average classification accuracy of 97.23% and outperforms all other feature representations, including bispectral invariant, local binary pattern, and histogram of oriented gradients using the same classifier on the same dataset. The proposed method is also compared and benchmarked with the other 12 existing techniques for classification of white blood cells into 10 classes over the same datasets and the results show that the proposed method achieves high accuracy in comparison with other approaches. The proposed method is also highly competitive in terms of computation and efficiency in comparison with other approaches.

There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.