Cooperative pixel clustering for accurate automatic inflamed appendix extraction from ultrasound images
by Kwang Baek Kim; Doo Heon Song; Hyun Jun Park
International Journal of Computational Vision and Robotics (IJCVR), Vol. 11, No. 6, 2021

Abstract: Reliable diagnosis and management of acute appendicitis is a difficult problem. Automatic extraction of inflamed appendix from ultrasonography is desirable to minimise the operator subjectivity of the ultrasound image analysis. In this paper, we propose a cooperative unsupervised machine learning approach to this automatic segmentation problem. The quantisation process is done by fuzzy ART with dynamic controlled vigilance parameter and fuzzy C-means pixel clustering with good parameter initialisation related with fuzzy ART. Two results are combined to produce a conservative but reliable inflamed appendix object formation. In the experiment using 80 DICOM format ultrasonographic images with inflamed appendix, the proposed method was successful in 77 cases or 96.25% correct by pathologists' evaluation which is much better performance than previous edge detection-based approach whose performance was less than 83%. This new approach is also relatively immune to the appendix shape which was a weak point of previous pixel clustering approaches.

Online publication date: Thu, 28-Oct-2021

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