Cooperative pixel clustering for accurate automatic inflamed appendix extraction from ultrasound images. (21st September 2021)

• Record Type: Journal Article
• Authors: Kim, Kwang Baek; Song, Doo Heon; Park, Hyun Jun
• Is Part Of: International journal of computational vision and robotics. Volume 11:Number 6(2021)
• Page Start: 640
• Page End: 652
• Publication Date: 2021-09-21
• Subjects: appendicitis -- ultrasound -- fuzzy ART -- fuzzy C-means -- FCM -- image quantisation; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

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.