Automatic segmentation of large bowl obstruction area with hough transform from erect abdominal radiograph images

Abstract

Large bowel obstruction is less frewuent but often appears acute and needs emergent treatment. Erect abdominal radiograph is usually the first imaging study performed in patients suspected of having large bowel obstruction. However, that mordality suffers from operator subjectivity thus a fully automatic computer aied tool is necessary. In this paper, we peopose an automatic large bowel feature (air-fluid region) segmentation method based on Canny edge detection and Hough transform. In experiment, the proposed method was successful in finding target region from large bowel obstruction patients’ radiographic images in all 30 cases provided. Whilie limited only applicable to the large bowel obstruction cases, the proposed method is practically feasible in application.

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

“…We then applied the Hough transform to locate suspicious LBO regions using Canny edge processing, as shown in [33]. The Hough transform can be defined as the transformation of a point in Cartesian space into a parameter space defined by the shape of the object of interest.…”

“…The Hough transform can be defined as the transformation of a point in Cartesian space into a parameter space defined by the shape of the object of interest. We used the technique explained in [33].…”

“…As observed from similar research on detecting suspicious regions with various pixel clustering algorithms [30], erect radiography enables visualizing such areas with image processing and fuzzy clustering algorithms. We also attempted to observe the effect of the Hough transform [31,32] using a traditional edge detection method to locate such regions from the LBO area without a machine learning technique [33]. Based on previous experience, located gas-filled areas from large-bowel images; however, the performance of such an algorithm is weak when the image contains noise.…”