Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5753 |
| _version_ | 1827665416679325696 |
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| author | Kwang Baek Kim Doo Heon Song Hyun Jun Park |
| author_facet | Kwang Baek Kim Doo Heon Song Hyun Jun Park |
| author_sort | Kwang Baek Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:27:54Z |
| format | Article |
| id | doaj.art-96b97360e0ec4054a32493719538d465 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:27:54Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-96b97360e0ec4054a32493719538d4652023-11-23T13:47:20ZengMDPI AGApplied Sciences2076-34172022-06-011211575310.3390/app12115753Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means ClusteringKwang Baek Kim0Doo Heon Song1Hyun Jun Park2Department of Artificial Intelligence, Silla University, Busan 46958, KoreaDepartment of Computer Games, Yong-In Art & Science University, Yongin 17145, KoreaDivision of Software Convergence, Cheongju University, Cheongju 28503, KoreaAccurate 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.https://www.mdpi.com/2076-3417/12/11/5753inflamed appendixappendicitissegmentationoutlier rejectionfuzzy c-meanspixel clustering |
| spellingShingle | Kwang Baek Kim Doo Heon Song Hyun Jun Park Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering Applied Sciences inflamed appendix appendicitis segmentation outlier rejection fuzzy c-means pixel clustering |
| title | Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering |
| title_full | Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering |
| title_fullStr | Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering |
| title_full_unstemmed | Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering |
| title_short | Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering |
| title_sort | robust automatic segmentation of inflamed appendix from ultrasonography with double layered outlier rejection fuzzy c means clustering |
| topic | inflamed appendix appendicitis segmentation outlier rejection fuzzy c-means pixel clustering |
| url | https://www.mdpi.com/2076-3417/12/11/5753 |
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