Demarcating Z-line and Gastric Folds Boundary Based on the Segmentation of the Lower Esophageal Sphincter Images

Abstract

Background and objective: The endoscopic diagnosis of pathological changes in the gastroesophageal junction including esophagitis and Barrett's mucosa is based on the visual detection of two boundaries: mucosal color change between esophagus and stomach, and top endpoint of gastric folds. The presence and pattern of mucosal breaks in the gastroesophageal mucosal junction (Z line) classify esophagitis in patients and the distance between the two boundaries points to the possible columnar lined epithelium. Since visual detection may suffer from intra- and interobserver variability, our objective was to define the boundaries automatically based on image processing algorithms, which may enable us to measure the detentions of changes in future studies.

Methods: To demarcate the Z-line, first the artifacts of endoscopy images are eliminated. In the second step, using SUSAN edge detector, Mahalanobis distance criteria, and Gabor filter bank, an initial contour is estimated for the Z-line. Using region-based active contours, this initial contour converges to the Z-line. Finally, by applying morphological operators and Gabor Filter Bank to the region inside of the Z-line, gastric folds are segmented.

Results: To evaluate the results, a database consisting of 50 images and their ground truths were collected. The average dice coefficient and mean square error of Z-line segmentation were 0.93 and 3.3, respectively. Furthermore, the average boundary distance criteria are 12.3 pixels. In addition, two other criteria that compare the segmentation of folds with several ground truths, i.e., Sweet-Spot Coverage and Jaccard Index for Golden Standard, are 0.90 and 0.84, respectively.

Conclusions: Considering the results, automatic segmentation of Z-line and gastric folds are matched to the ground truths with appropriate accuracy.

Keywords: Adenocarcinoma; Barrett's esophagus; demarcating Z-line and gastric folds boundary; segmentation of lower esophageal sphincter endoscopy images.

Figure 1

Shematic Ilustration of abnormal LES image, adapted from Figure 1 of[3]

Figure 2

Proposed method

Figure 3

Exponential and fractional functions used in non-linear diffusion filters for k = 5 and α = 1

Figure 4

(a) Original image (b) Denoised image, artifacts and other highpass noises are removed but Z-line edge is preserved

Figure 5

(a) Original LES image, (b) Output of NUI correction

Figure 6

(a) Histogram of Red channel, taken from border (mean = 20, variance = 15.48) and from normal tissue (mean = 184.13, variance = 50.37), (b) Histogram of saturation channel, taken from SR and IJ (mean = 7.69, variance = 19.91) and normal tissue (mean = 101.52, variance = 19.68)

Figure 7

Comparison of red, blue and green histograms for lumen and tissue (a) red channel (b) green channel (c) blue channel

Figure 8

(a) Rough estimation of lumen, (b) detected lumen, (c) Locating the intensity of the initial seed in the histogram of estimated region

Figure 9

Procedure of automatic extraction of ROI, (a) Original image (b) Border extraction, (c) Part b, plus SR and IJ regions excluded, (d) Part c, plus lumen region excluded

Figure 10

Initial contour extraction

Figure 11

Procedure of removing unwanted edges: (a) SUSAN edge detector output, (b) Removing unwanted edges using Mahalonobis distance, (c) Circle fitting for a rough estimation of gastric folds, (d) Output of Gabor filter bank, (e) First estimation of gastric folds, (f) Final initial contour for Z-line segmentation

Figure 12

Two clusters formed by pixels inside (Orange) and outside (Pink) of Z-line

Figure 13

Masks used for choosing pixels around each edge. Yellow square shows the detected edge, orange and pink squares denote chosen pixels around each edge. (a) 0° mask, (b) 45° mask, (c) 90° mask, (d) 135° mask

Figure 14

Real parts of Gabor filter for 0° (a) to 157.5° (h) with step of 22.5°, (F0 = 4cycle/pixel, σ= β= 2)

Figure 15

(a) Original image, (b) Border expansion

Figure 16

Four examples of automatic Z-line segmentation. Columns 1 (a and e) and 3 (c and g) are our output and columns 2 (b and f) and 4 (d and h) are clinicians ground truths

Figure 17

Two examples of automatic gastric folds segmentation. Left column (a and e) shows our output and three next columns (b, c, d, f, g, and h) show the ground truth of three different clinicians