Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

Abstract

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In this paper, we propose a robust automatic segmentation method for inflamed appendix identification to mitigate abovementioned difficulties. We use outlier rejection fuzzy c-means clustering (FCM) algorithm within a double-layered learning structure to extract the target inflamed appendix area. The proposed method extracts the target appendix in 98 cases out of 100 test images, which is far better than traditional FCM, standard outlier FCM, and double-layered learning with FCM in correct extraction rate. Furthermore, we investigate the outlier rejection effect and double layered learning effect by comparing our proposed method with standard double-layered FCM and the standard outlier-rejection FCM. In this comparison, the proposed method exhibits robust segmentation results in accuracy, precision, and recall by 2.5~5.6% over two standard methods in quality with human pathologists’ marking as the ground truth.

Current Research in Future Information and Communication Engineering 2022

“…Two papers focused on biomedical image classification using fuzzy-based learning approaches. The first one, by Kim et al [4], proposed and evaluated a robust automatic segmentation method using double-layered outlier rejection fuzzy c-means (DORFCM) to identify an inflamed appendix from abdominal ultrasound images. The second one, by Kim et al [5], also proposed and evaluated a hierarchical combination of the fuzzy unsupervised learning component (FCM) and supervised learning component (FMM: fuzzy max-min neural network) in diabetes diagnosis, a highly noisy domain with not much labelled data and many missing values in the dataset.…”

Combining Hough Transform and Fuzzy Unsupervised Learning Strategy in Automatic Segmentation of Large Bowel Obstruction Area from Erect Abdominal Radiographs

“…Automatic segmentation with pixel-level clustering may be a solution to this human error. Developing such system has become challenging in the case of medical imaging owing to poor contrast and noise during acquisition [22,23], but has shown positive effects in many medical image analysis domains [24][25][26][27][28][29].…”