K mean clustering based automated segmentation of overlapping cell nuclei in pleural effusion cytology images

Automated segmentation of cell nuclei is crucial for the early diagnosis of cancer as the characteristics of the cell nuclei are mainly associated with the assessment of malignancy. Only a few research work has been done on automated segmentation of cell nuclei on cytology pleural effusion images, which is poorly handled by previous methods. In addition, cytology pleural effusion image itself is still challenging due to the poor contrast of images, a variety of cells, and overlapping cells. To deal with the remained problems, this paper presented the algorithm for automated segmentation of cell nuclei in pleural effusion cytology images which contain touching and overlapping cells. First, the preprocessing step is carried out to reduce noise and enhance the contrast using the median filter and CLAHE respectively. The cell nuclei are segmented using K Mean Clustering algorithm in LAB color space. Then, the boundaries are corrected and non-nuclei regions are eliminated by the morphological operations. Finally, the overlapping cell nuclei are isolated depending on the watershed method, subsequently, boundaries of isolated cell nuclei are estimated using ellipse fitting method. The proposed system is evaluated on the local dataset containing 35 images of cytology pleural effusions with normal benign and cancer cells. The experimental results yield the accuracy of Precision= 0.90, Recall=0.89, F-meas-ure=0.89, Dice Similarity Coefficient=94% and Piccard In-dex=89% respectively. The obtained results are verified and compared with the ground truth images manually annotated by experts.