Diagnosis of hand ganglion cyst using free space broadband frequency modulated fNIR imaging system

Sultan, Ebraheem; Ahmad, Nizar J.; Daryoush, A.S.

doi:10.1109/benmas.2014.7529485

Cited by 1 publication

(1 citation statement)

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“…Automatic segmentation in ultrasound images, however, can be difficult for numerous reasons such as insufficient contrast and resolution of image, speckle noise, which is inherent property of ultrasound imaging modality [ 12 ]. For the ganglion cyst segmentation problem, a computerized cyst measuring system with different modality- functional infrared (fNIR) spectroscopy was introduced [ 13 ], as well as automatic retinal cyst detector by optical coherence tomography [ 14 ]. However, those works are not appropriate for segmenting ganglion cyst with ultrasonographic images in fully automatic approach (such as the approach proposed in this paper).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Automatic Segmentation of Wrist Ganglion Cysts Using DBSCAN and Fuzzy C-Means

2021

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Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert’s decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability.

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Section: Introductionmentioning

confidence: 99%