Extraction and Recognition of Myeloma Cells in Microscopic Images of Bone Marrow Aspiration

Abstract

Background: Plasma cells are developed from B lymphocytes, a type of white blood cells generated in the bone marrow. The plasma cells produce antibodies to fight bacteria and viruses and stop infection and disease. In multiple myeloma, a cancer of plasma cells, collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. Sometimes, existence of infection in body causes plasma cells increment, which could be diagnosed wrongly as multiple myeloma. Diagnosis of myeloma cells is mainly based on nucleus to cytoplasm ratio, compression of chromatin at nucleus, perinuclear zone in cytoplasm and etc.; so, because of depending final decision on human’s eye and opinion, error risk in decision may be occurred. In this study, we presented an automatic method using image-processing techniques for myeloma cells diagnosis from bone marrow smears. Methods: First, via contrast enhancement algorithm and k-means clustering, nucleus and cytoplasm of cells were completely extracted from bone marrow images. Then, for splitting connected nuclei and clump cells, two algorithms based on bottleneck and watershed methods were applied. Finally, via feature extraction from the nucleus and cytoplasm, myeloma cells were separated from normal plasma cells. Findings: The algorithm was applied on 30 digital images contained 64 normal plasma cells and 73 myeloma cells. Applying the automatic identification of myeloma cells on provided database showed the accuracy of 99.27%. Conclusion: In this study, an automatic method for detection and classification of plasma cells from myeloma cells in microscopic images of bone marrow aspiration was proposed.

Keywords

• B-Cells
• Plasma cell myeloma
• Image analysis