Ankon Technologies Co., Ltd

Anx IP Holding Pte. Ltd.

Anx Anx IP Holding Pte. Ltd.

Wenjin Yuan

Hao Kai Zhang

Hang Zhang

Hui Liu

Zhiwei Huang

Xiaodong Duan

Guohua Xiao

Xinhong Wang

A method of three-dimensional reconstruction of capsule endoscope images is described. The method combines two algorithms for obtaining image depth information values to improve the calculation accuracy of image depth values, and thus to increase the image three-dimensional reconstruction rate and improve the image identification precision.

Capsule endoscope image three-dimensional reconstruction method, electronic device, and readable storage medium

The present invention discloses a method, device and medium for structuring a capsule endoscopy report text. The method includes: annotating the report text using an annotation model; storing each named entity classification label in the report text in a hierarchical tree structure according to the annotation information to form a tree structure diagram; parsing the tree structure diagram, extracting abnormal structure data and time parameters, and storing the abnormal structure data and time parameters in a panel data structured manner to form an abnormal structure panel table and a time parameter panel table. The present invention can automatically annotate the capsule endoscopy report through the annotation model, and output parameters of different amount of information in different structures, and quantitative and accurate quality control of the capsule endoscopy process and examination results, which provide sufficient convenience for the electronic medical information of the capsule endoscopy.

Method, device and medium for structuring capsule endoscopy report text

A method for stitching images of a capsule endoscope, an electronic device, and a readable storage medium are provided. The method comprises: performing image rectification and circular edge masking on original images to form pre-processed images, and performing image enhancement (S); completing detection and pairing of feature points (S); calculating a transformation model of all pre-processed images to a same optimal plane according to a set of the feature points (S); performing projective transformation for each enhanced image to a same coordinate system (S); and stitching images according to an obtained sequence to form a fused image for output (S), thus expanding the field of view of a single image of the capsule endoscope.

Method for stitching images of capsule endoscope, electronic device and readable storage medium

The present invention discloses a capsule endoscope system, a method of identifying stained area in endoscopic images, and a computer readable storage medium. The method comprises: obtaining a basic image taken by a photographing device; removing abnormal pixels from the basic image and recording it as a processed image; obtaining the hue, saturation and value of each pixel in the processed image of HSV format; setting a first condition that the hue is in a D1 range, the saturation is in a D2 range, and the value is in a D3 range; obtaining the b component of each pixel in the processed image of Lab format; setting a second condition that the b component is in a D4 range; collecting and processing the pixels that meet the first condition and/or the second condition to form a set C; calculating the initial range area S1 of the set C.

Capsule endoscope system, method of identifying stained area in endoscopic images and computer readable storage medium

The present invention provides a transfer learning based capsule endoscopic images classification system. The system removes the capsule endoscopic images with an average brightness value beyond the preset threshold, and removes the capsule endoscopic images without details based on image brightness standard deviation and image brightness gradient. The system also removes similar images from the capsule endoscopic images using optical flow method, classifies the capsule endoscopic images according to the corresponding anatomical structure, and obtains the classified capsule endoscopic images list arranged in chronological order. The system further determines and labels the position of the first image of each specific anatomical structure in the classified capsule endoscopic images list arranged in chronological order.

Transfer learning based capsule endoscopic images classification system and method thereof

A system and a method for preprocessing capsule endoscope images are provided. The capsule endoscopic image preprocessing system include an in vitro image removal module, an invalid image removal module, a digestive tract image classification module, a lesion and anatomical structure identification module, and a lesion and anatomical structure redundant image removal module. The capsule endoscopic image preprocessing system removes in vitro images and invalid images from capsule endoscopic images, classifies the capsule endoscopic images according to different parts of the digestive tract, identifies lesion and anatomical structures in the classified capsule endoscopic images; and removes redundant lesion and anatomical structure images according to the lesion and anatomical structures.

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Wuhan, China

Wenjin Yuan