Bleeding detection in wireless capsule endoscopy videos — Color versus texture features

Abstract

Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-ofthe-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system.

Artificial intelligence and capsule endoscopy: unravelling the future

“…In August of 2019, Pokorelov et al developed a combined color and texture algorithm with excellent computational cost and efficiency. Using 300 bleeding frames and 200 nonbleeding or normal frames for the training dataset (500 frames) and 500 bleeding and 200 nonbleeding frames (700 frames) for the testing dataset, they were able to obtain a sensitivity, specificity and accuracy of 97.6%, 95.9% and 97.6%, respectively [ 34 ]. Also, in August of the same year, Aoki et al developed a CNN method that compared the time and effectiveness of videocapsule reading by 2 processes: (A) endoscopist-alone readings; and (B) endoscopist readings after a first screening by the proposed CNN.…”

Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis

“…Konstantin et al have introduced a bleeding detection technique utilizing the texture and color features that would provide the complete color information. Nevertheless, the color information technology provides lower performance results compared to the other existing methods [10]. The frequency spectrum of characteristics pattern is based on the normalized gray level co-occurrence matrix and achieved a satisfactory bleeding detection rate [11].…”

Small intestine bleeding detection using color threshold and morphological operation in WCE images

“…Konstantin et al have introduced a bleeding detection technique utilizing the texture and color features that would provide the complete color information. Nevertheless, the color information technology provides lower performance results compared to the other existing methods [7]. The frequency spectrum of characteristics pattern is used based on normalized gray level co-occurrence matrix and also achieved a satisfactory bleeding detection rate [8].…”