A hybrid cell image segmentation method based on the multilevel improvement of data.

Over the years, several methods have been developed for the segmentation of cell images. Most of the related techniques operate directly on the raw data (noisy cell samples) of the medical image which leads to adverse effects on the structure of leucocytes because the medical images are affected by multiple distortions (varying illumination, deficient background light intensity, and non-uniform staining). To overcome these problems, we came up with an improved solution that performs the qualitative enhancement of cell images for the smooth extraction of cell-nucleus. Although various segmentation methods have adopted an image improvement operation in practice. These methods also amplify the magnitude of image noise which leads to over-sampling and under-sampling of data points. This mis-labelling of data points is minimized by the developed approach which adopts a collaborative fusion strategy (CNN and Nuclear-norm approach) for the qualitative improvement of cell images. The enhanced cell samples were forwarded to the U-net (deep learning model) model for the semantic segmentation of cell images. The performance evaluation of the model was performed on three biomedical cell imaging datasets, which include the ALL-IDB (99.89% accuracy, 99.51% recall, and 99.01% precision), CellaVision (99.68% accuracy, 98.75% precision, and 97.94% specificity) and JTSC (98.45% accuracy, 97.42% precision, and 97.21% specificity) dataset. The results were compared with the state-of-art methods in which the adopted hybrid approach has overpowered the related techniques in the quantitative and qualitative domains.

An enforced block diagonal low-rank representation method for the classification of medical image patterns.

Low-rank representation based methods have been used on a variety of medical imaging databases for the segmentation and classification of biomedical images. The subspace segmentation of the data is performed by generating the block diagonal coefficient matrix. Whereas, the data is classified by performing the partitioning of the low-rank representation matrix. There exist several such methods for analysing medical images. The major difference between them lies in the construction of the data dictionary. Most of the time, the input data pattern is used as the dictionary for learning the representation matrix. The direct use of the input data for learning the representation degrades the performance of the model because medical images are subjected to outliers of multiple types, which include environmental lighting, image appearance and varying illumination. These types of errors induce noise in the data. It has been observed that the representation-based model is robust when the training data is clean. If the training data contains corrupted subsamples, the performance of the model drops down. We have addressed the mentioned problem by adopting a class-wise dictionary learning approach. In which the pattern of each class is learnt as the set of tuples in the dictionary. The model has been evaluated on several medical imaging datasets, which includes the Break-his dataset, ALL-IDB, biomedical images, covid CT and chest X-ray. The classification performance of the model is best for the biomedical database (99.16%) followed by the Covid dataset (94%), ALL-IDB database (93.47%) and Break-his dataset (93%).