An Approach to Differentiate Cell Painted ER and Cytoplasm Using Zernike Moment Descriptor and Multilayer Perceptron

Differentiation of cell organelle characteristics from microscopic images is a challenging task due to its intricate structural details. In this work, an attempt has been made to categorize Endoplasmic Reticulum (ER) and cytoplasm using orthogonal Zernike moments and Multilayer Perceptron (MLP). For this, Cell painted public source dataset comprising of ER and cytoplasm are considered. Zernike moments for different orders and repetition of the azimuthal angle are extracted to characterize the shape features. The extracted features are validated using MLP classifier for differentiating ER and cytoplasm. The prediction accuracy for variations in the number of hidden layers are evaluated. The experimental results show that the accuracy varies as the size of hidden layer increases. The extracted features with MLP achieved an accuracy of 85% with a hidden layer size of 5. The receiver operating characteristic curve (ROC) demonstrates the distinguishing power of MLP classifier with AUC=0.92. This study suggests that the proposed framework can be employed for analyzing the morphological variations of cell organelles due to chemical perturbations, genome variations and cytotoxic effects using the combination of Zernike shape descriptor and MLP. The orthogonality property of Zernike shape descriptor provides independent unique features which reduce redundancy and improve prediction accuracy for large datasets.