This chapter contributes to the study of uncertainty of signal dimensions within a microscopic image of blood sample. Appropriate colocalization indicator classifies the leukocytes in the region of interest having ragged boundaries. Signal transduction has been interpreted using correlation function determined fluorescence intensity in proposed work using just another colocalization plugin (JaCoP). Dependence between the channels in the colocalization region is being analysed in a linear fashion using Pearson correlation coefficient. Manders split, which gives intensity, is represented in a channel by co-localizing pixels. Overlap coefficients are also being analysed to analyse coefficient of each channel. Li's intensity correlation coefficient is being used in specific cases to interpret the impact of staining.
Top2. Literature Survey
“Immunohistochemical” slide image registration accuracy depends on the field of view of few cells. Registration accuracy is achieved with biomarker colocalization using an elastix framework based on dynamic resolution levels (Moles Lopez et al., 2015). The classification of leukocytes based on their shape and lobes of nucleus is given as follows. It can be mononuclear which includes “Monocytes” and “Lymphocytes”. The other contains granules named as granulocytes, which includes Neutrophils and Eosinophils. The extraction of WBC cells from blood samples is followed by separation of cytoplasm and nucleus thereby further enhanced classification has been done in (Putzu et al., 2014). Marker controlled watershed has been used for segmentation based on cell nucleus. Subsequently, classification has been done to separate WBC and RBC (Miao & Xiao, 2018). However, misclassification may result in improper movement of WBC leading to cell adhesion.
A review of colocalization techniques has been discussed with “Manders co-occurrence” (MOC) which considered pixel intensity (Manders et al., 1993). The limitation of MOC is that, it can be affected by useless signal. Similarly, Pearsons Correlation Coefficient (PCC) based on interdependency may result that depended on threshold values (Aaron et al., 2018). Localization using colour transformation methods and subsequent segmentation of cytoplasm via region growing, watershed and finally classification is done using support vector machine has been done using bone marrow images (Liu et al., 2019). Leukocyte nuclei segmentation using channel splitting with blue and green channel can reduce the time incurred to analyse the blood cell image (Wang & Cao, 2019). Both spatial and spectral features for segmenting the nucleus and leucktocyte region was discussed. Spatial feature used morphological operations, whereas spectral features used support vector machine (Duan et al., 2019). Feature classifying benign and malignant cells incorporating convolutional neural network and statistical property has been discussed with Salp Swarm Algorithm (Sahlol et al., 2020). But the work included, most features which are relevant and excludes noise. Arbitrary shape of biomedical image has been done using colocalization in a cell of interest with dilated radius(CIRCOAST) (Corliss et al., 2019). Molecular interaction of irregular shape has been discussed using Geo-coPositioning system. It combines an object based method to intensity based method. Object based method provides resilience against noise and content of fluorescence signal is estimated using intensity based method (Lavancier et al., 2020). Biological processing with “Digital Lensless holographic microscopy” states that ImageJ plugin has been used for interoperability for calculating numerical simulation (Trujillo et al., 2020). Hence this work used ImageJ with JaCoP plugins for analysis.
Semantic segmentation has been used initially after pre-processing the blood cell images the pixel level features are extracted using deep convolutional encoder and decoder. The accuracy in classifying the RBC, WBC and platelets are better using Intersection of Union and Boundary Score (Shahzad et al., 2020).
2.2 Problem Definition
Fluorescence spectra are not separated well in image acquisition. Misalignment of signal with imperfect representation of leukocytes cells with nucleus and cytoplasm along with its overlap can result in various quantitative values or volumetric changes leading to wrong diagnosis. Thus colocalization of individual images has to be done manually, to interpret the intracellular markers.