Segmentation and counting of multiple myeloma cells using IEMD based deep neural network.

In biomedical image analysis, segmentation of cell nuclei from microscopic images is a highly challenging research problem. In the computer-assisted health care system, the segmented microscopic cells have been used by many biological researchers for the early prediction of various diseases. Multiple myeloma is one type of disease which is also term as a plasma cell cancer.

Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images.

In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task.