Segmentation of Effective Cells in Multiple Myeloma Cancer Using Deformable Models and K-Means Clustering

Multiple myeloma is the second most common hematopoietic cancer. This disease is caused by the cancerous category of cells called plasma cells. Detecting and counting plasma cells provide valuable information for pathologists to diagnose this disease. The manual counting and considering of plasma cells are time consuming and due to the tedious nature of this process, it is subject to error. Thus, a computer-aided tool for pathologists to help in the diagnostic process can be very useful. For this purpose, this research presented a computer tool for segmentation of effective cells in multiple myeloma from microscopic images.
In proposed method, after improving the quality of the images using histogram matching and median filter, the cells were extracted using the Chan-Vese deformable model. In addition, for splitting touching cells, the Modified Watershed algorithm was used. Then, the nuclei were extracted applying the k-means clustering method.
Findings: The proposed method was evaluated on 30 microscopic images containing 370 cells. The calculated results of the proposed method showed that similarity measures, sensitivity, precision, accuracy and Dice Similarity Coefficient (DSC) respectively were 89.01%, 89.95%, 97.71%, 98.63%, and 93.86% for cell segmentation, and 91.43%, 92.48%, 96.13%, 98.53%, and 95.47% for nucleus segmentation.
In this research, a novel method was presented for segmentation and extraction of effective cells in the diagnosis of multiple myeloma cancer from microscopic images using deformable models and clustering method. The evaluation results show that the proposed algorithm have improved segmentation performance compared to the previous methods.