Subspace Segmentation and Clustering of Medical Image Data Based on Low-Rank Representation
Ishfaq Majeed Sheikh, Manzoor Ahmad Chachoo · 2021 Innovations in Intelligent Systems and Applications Conference (ASYU) · 2021
Biomedical cell image contains different types of components. The segmentation of these components is essential for characterizing the overall structure of leucocytes. However, the task is challenging because the data generally possess irregular patterns, varying eccentricity and congestion of inter class substructures. In order to perform the automatic segmentation of leucocytes, many approaches in machine learning have been proposed. Most of the recent methods utilize k-means and fuzzy c-means algorithms. These methods suffer from the under-segmentation and the oversegmentation of data points. We have adopted an efficient Low Rank Representation (LRR) method for the segmentation of cell image patterns. Its overall segmentation performance is very high in comparison to the k-means and fuzzy c-means segmentation methods. It discriminates sub structures by incorporating multiple feature data into the joint representation matrix. The main improvements of our adopted approach, include the global geometric representation of different cell patterns and the control of different sources of noise in the data. Data points of the cell image are evolved with the basic assumption as x = m + n. Where x is the input observation,$m$is the set of data points that lies in the correct subspace and$n$represent the noise in the data. The performance of the model was evaluated on the different types of medical images. It has reported the clustering accuracy of 97.98% on the biomedical database. Whereas an accuracy of 93.1 % was reported on ALL-IDB cell dataset.