Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering  
[PERSON]  
1 Department of Artificial Intelligence, Silla University, Busan 46958, Korea 1,1  
gargrene appendicitis, in which necrosis and serous exudate occur throughout the wall of the appendix. The diagnosis of acute appendicitis is a challenging task. Detection of appendiceal neoplasms at pre-operative imaging is important because it may change the surgical approach and prevent additional surgery [3]. However, more than half will have different clinical and radiological manifestations [4] and there exist non-negligible amount of age and gender related false positive cases, such as pelvic inflammatory disease and other gynecologic conditions for childbearing age women [5; 6]. Such misdiagnosis or delayed diagnosis may increase the incidence of perforation, peritonitis, and negative laparotomy, which can affect the morbidity and mortality of patients [7]. Thus, it is important to have a quick and accurate acute appendicitis's diagnosing methodology for patients appealing acute abdominal pain. Among possible diagnostic modalities, ultrasound examination is usually the first imaging test performed, especially for the pediatric and young adult populations, as well as patients in pregnancy who are very vulnerable to the radiation. However, if the first opinion of ultrasound examination is in indeterminate status with a strong clinical suspicion of appendicitis, a second ultrasound examination by well experienced staff or a Computed tomography (CT) scan or Magnetic resonance imaging (MRI) scan is necessary for reliable diagnosis [8; 9]. Appendiceal ultrasound is most accurate when the appendix is clearly identified during the examination and relevant sonographic criteria is applied to determine the presence of appendiceal inflammation [10]. Traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, wall thickness, and hyperaemia, rely on complete identification or visualization of the appendix. However, according to the recent report, the sonographic appendix visualization rate can vary significantly [11]. It becomes more challenging when abdominal ultrasound could not correctly visualize the inflamed appendix [12]. Moreover, while the reported specificity of ultrasound is almost equal to CT, the reported sensitivity for diagnosis by ultrasound varies wider in the literature (67-100%) and showed high operator dependency [13]. A correct ultrasound technique and knowledge about imaging features of appendicitis's improve the detection of inflamed appendix and therefore may help to make the diagnosis without performing unnecessary CT scans [14]. To avoid such operator subjectivity and unclear visualization of inflamed appendix from abdominal ultrasound, we need robust automatic segmentation tool to extract inflamed appendix. Simple edge detecting methods relying on histogram analysis with thresholds [15; 16; 17] are inaccurate when the brightness contrast of the image is not remarkably high. The reported performance to extract inflamed appendix were no better than 83.3% due to information loss during edge linking process. To identify the target object from low contrasted ultrasonography, pixel clustering method is a viable alternative and has been proven to be effective in engineering and medical domains [18; 19; 20; 21; 22]. In this pixel-based approach, the automatic segmentation process is usually divided into two phases--brightness enhancement for noise reduction and quantization-based object formation. The basic predictor of locating inflamed appendix is the fascia line [23]. Fuzzy Adaptive resonance theory (ART) based quantization algorithm for inflamed appendix extraction [24] was immune to that brightness sensitivity and showed better extraction rate than edge detecting methods, but it was found that the method was weak when the shape of the inflamed appendix has a certain type.