Kim Kwang Baek, Park Hyun Jun, Song Doo Heon, Han Sang-suk
Department of Computer Engineering, Silla University, Busan 617-736, Republic of Korea.
Department of Computer Engineering, Pusan National University, Busan 609-735, Republic of Korea.
Comput Math Methods Med. 2015;2015:389057. doi: 10.1155/2015/389057. Epub 2015 May 18.
Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.
超声检查(US)在临床疑似阑尾炎患者的诊断和管理中起着关键作用,阑尾炎是最常见的腹部外科急症。在阑尾炎的各种超声表现中,阑尾外径最为重要。因此,在超声图像上清晰勾勒出阑尾至关重要。在本文中,我们提出了一种新的智能方法,可从腹部超声图像中自动提取阑尾,作为为医学从业者开发此类智能工具的基本构建模块。鉴于阑尾位于腹横筋膜线下方的下腹部器官区域,我们采用一系列图像处理技术来正确找到筋膜线。然后我们将模糊ART学习算法应用于器官区域,以准确提取阑尾。实验验证了所提出的方法在提取阑尾方面具有很高的准确性(40例中有38例成功)。