Using Marker‑Controlled Watershed Transform to Detect Baker’s Cyst in Magnetic Resonance Imaging Images: A Pilot Study

Nowadays, magnetic resonance imaging (mri) has a high ability to distinguish between soft tissues because of high spatial resolution. image processing is extensively used to extract clinical data from imaging modalities. in the medical image processing field, the knee’s cyst (especially baker) segmentation is one of the novel research areas. there are different methods for image segmentation. in this paper, the mathematical operation of the watershed algorithm is utilized by matlab software based on marker‑controlled watershed segmentation for the detection of baker’s cyst in the knee’s joint mri sagittal and axial t2‑weighted images. the performance of this algorithm was investigated, and the results showed that in a short time baker’s cyst can be clearly extracted from original images in axial and sagittal planes. the marker‑controlled watershed segmentation was able to detect baker’s cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of mri pathologies.