Bone Marrow Microscopic Data (plasma cell lineage images)


Bone Marrow Microscopic Data (plasma cell lineage images)

This folder contains bone marrow microscopic images. These images are categorized into two groups: Normal Plasma Cells and Myeloma Cells.

Dataset of Bone Marrow Microscopic Images

This folder contains bone marrow microscopic images.‎‎ These images are categorized into two groups: Normal Plasma Cells and Myeloma Cells.‎‎ For data acquisition, a digital camera (Sony DSC-H9) coupled on an optical microscope (Olampus-CH40RF200) were used and 50 data were captured for extraction and recognition of myeloma cell in microscopic bone marrow aspiration images.‎

If you use these data please cite the following paper:
Saeedizadeh Z, Talebi A, Mehri-Dehnavi A, Rabbani H, Sarrafzadeh O.‎ Extraction and Recognition of Myeloma Cell in Microscopic Bone Marrow Aspiration Images.‎ J Isfahan Med Sch., 32(310), 3rd week, Jan.‎ 2015.‎

Abstract

Background: Plasma cells are developed from B lymphocytes, a type of White Blood Cells that is generated in the bone marrow.‎ The plasma cells produce antibodies to fight with 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 cell’s 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.‎ This study presents an automatic method using image processing techniques for myeloma cells diagnosis from bone marrow smears.‎
Methods: In this study, at first by contrast enhancement algorithm and k-means clustering, nucleus and cytoplasm of cells are completely extracted from bone marrow images.‎ Then, for splitting of connected nuclei and clump cells, two algorithms based on bottleneck and watershed methods are applied.‎ Finally by feature extraction from the nucleus and cytoplasm, myeloma cells are separated from normal plasma cells.‎
Findings: This algorithm is applied on 30 digital images which are 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 bone marrow aspiration images are proposed.‎

Keywords: B-Cells, Plasma cell myeloma, Image analysis

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