Open AccessUsing marker-controlled watershed transform to detect Baker's cyst in magnetic resonance imaging images: A pilot study
Author(s) -
Sadegh Ghaderi,
Kayvan Ghaderi,
Hamid Ghaznavi
Publication year - 2022
Publication title -
journal of medical signals and sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 21
ISSN - 2228-7477
DOI - 10.4103/jmss.jmss_49_20
Subject(s) - magnetic resonance imaging , segmentation , computer science , sagittal plane , artificial intelligence , image segmentation , computer vision , image processing , cyst , watershed , matlab , medicine , radiology , image (mathematics) , operating system
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.