Automatic Extraction of Appendix from Ultrasonography with Self-Organizing Map and Shape-Brightness Pattern Learning

Abstract

Accurate diagnosis of acute appendicitis is a difficult problem in practice especially when the patient is too young or women in pregnancy. In this paper, we propose a fully automatic appendix extractor from ultrasonography by applying a series of image processing algorithms and an unsupervised neural learning algorithm, self-organizing map. From the suggestions of clinical practitioners, we define four shape patterns of appendix and self-organizing map learns those patterns in pixel clustering phase. In the experiment designed to test the performance for those four frequently found shape patterns, our method is successful in 3 types (1 failure out of 45 cases) but leaves a question for one shape pattern (80% correct).

Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

“…As stated in section 1, our previous approach using SOM [26] works nicely in most cases but we found that SOM based pixel clustering was subtle when there was a loss of intensity information during its repetitive learning process to select the single winning neuron. In failed extraction cases, it could not locate the inflamed appendix since the lost intensity information misguided the software to remove the appendix object as noise.…”

“…Intelligent pixel clustering methods [6,12,25,26] are designed to enhance the brightness contrast and form an appendix object from US. A method uses fascia as a predictor of the appendix location and uses fuzzy binarization techniques to determine the appendix boundaries with various image processing algorithms [25].…”

“…We further developed the automatic extraction of inflamed appendix with Self-organizing Map (SOM) learning [26]. And that approach is very successful in correct extraction rate except a long oval shape type appendix whose brightness contrast is very low compared with surroundings.…”

“…In this paper, we propose a Fuzzy C-Means (FCM) based approach in automatic extraction of inflamed appendix to overcome the drawbacks found in [26]. FCM has been applied to medical imaging domain successfully [16,27] but has static initialization problem like K-means [28].…”

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

“…Instead, pixel clustering approaches have been successfully applied to detect the target organ from ultrasonography or X-ray images. Some examples of such approaches include detecting brain tumor [13,14], brachial artery [15], cervical vertebrae [16], lung cancer [17], inflamed appendix [18], ganglion cyst [19] and breast image segmentation [20]. Thus, in this paper, we take K-means based pixel clustering approach [16,21] to this automatic segmentation problem.…”