  Small intestinal capsule endoscopy is the mainstream method for inspecting
small intestinal lesions,but a single small intestinal capsule endoscopy will
produce 60,000 - 120,000 images, the majority of which are similar and have no
diagnostic value. It takes 2 - 3 hours for doctors to identify lesions from
these images. This is time-consuming and increase the probability of
misdiagnosis and missed diagnosis since doctors are likely to experience visual
fatigue while focusing on a large number of similar images for an extended
period of time.In order to solve these problems, we proposed a similar wireless
capsule endoscope (WCE) image screening method based on structural similarity
analysis and the hierarchical clustering of visually salient sub-image blocks.
The similarity clustering of images was automatically identified by
hierarchical clustering based on the hue,saturation,value (HSV) spatial color
characteristics of the images,and the keyframe images were extracted based on
the structural similarity of the visually salient sub-image blocks, in order to
accurately identify and screen out similar small intestinal capsule endoscopic
images. Subsequently, the proposed method was applied to the capsule endoscope
imaging workstation. After screening out similar images in the complete data
gathered by the Type I OMOM Small Intestinal Capsule Endoscope from 52 cases
covering 17 common types of small intestinal lesions, we obtained a lesion
recall of 100% and an average similar image reduction ratio of 76%. With
similar images screened out, the average play time of the OMOM image
workstation was 18 minutes, which greatly reduced the time spent by doctors
viewing the images.
