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. 2021 Dec 28;12(1):84-89.
doi: 10.4103/jmss.JMSS_49_20. eCollection 2022 Jan-Mar.

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

Sadegh Ghaderi  1 , Kayvan Ghaderi  2 , Hamid Ghaznavi  3
Affiliations

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

Sadegh Ghaderi et al. J Med Signals Sens. .

Abstract

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.

Keywords: Baker's cyst; image processing; magnetic resonance imaging; marker-controlled watershed transform.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1
Figure 1
Diagram of all steps during image processing
Figure 2
Figure 2
Preprocessing step on sagittal (a-c) and axial (d-f) plane images. Gaussian (b and e) and Sobel (c and f) filters are utilized for sagittal and axial images
Figure 3
Figure 3
Image (a) is an axial plane and image (b) is a sagittal plane of the knee, with a Baker's cyst
Figure 4
Figure 4
Using the Gaussian (c and f) and Sobel (b and e) filters and its effects on the image histogram (values in b and e histogram are expressed in the form of ×105)
Figure 5
Figure 5
Foreground section of segmentation process, upper and lower row images show axial (a-d) and sagittal (e-h)images, respectively
Figure 6
Figure 6
Background section of the segmentation process, upper and lower row images show axial (a-d) and sagittal (e-h) images, respectively
Figure 7
Figure 7
Superimposing foreground and background markers on axial (a) and sagittal (b) images
Figure 8
Figure 8
Output images, colored watershed label matrix (a and c) and colored labels transparently superimposed on the original image (b and d)
See this image and copyright information in PMC

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