Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.

In this study, under the assumption that a robot (1) has a remotely controllable yawing camera and (2) moves in a uniform linear motion, we propose and investigate how to improve the target recognition rate with the camera, by using wireless feedback loop control. We derive the allowable data rate theoretically, and, from the viewpoint of error and delay control, we propose and evaluate QoS-Hybrid ARQ schemes under data rate constraints. Specifically, the theoretical analyses derive the maximum data rate for sensing and control based on the channel capacity is derived with the Shannon-Hartley theorem and the path-loss channel model inside the human body, i.e. CM2 in IEEE 802.15.6 standard. Then, the adaptive error and delay control schemes, i.e. QoS-HARQ, are proposed considering the two constraints: the maximum data rate and the velocity of the camera's movement. For the performance evaluations, with the 3D robot simulator GAZEBO, we evaluated our proposed schemes in the two scenarios: the static environment and the dynamic environment. The results yield insights into how to improve the recognition rate considerably in each situation.

This paper proposed patient friendly capsule endoscopy (CE) for not only screening but also treatment. Two different types of CEs with an Internet utility were investigated. The first type used magnetic navigation in the stomach and colon for screening. Magnetic navigation enabled the capsule to explore the whole of the gastrointestinal tract with less risk of missing lesions and complete the screening within the battery life. The system's design was patiently friendly as it allowed the subjects to leave the hospital after the capsule had been navigated in the stomach. The second investigated two different therapeutic robotic endoscopes. Both prototypes were driven by DC motors and controlled remotely via the internet. In addition, they were equipped with therapeutic tools and each prototype's ability with the tools was assessed. The investigation showed it was possible to remotely control both prototypes and operate therapeutic tools via the Internet. The investigation identified areas for improvement, such as size, connection speed, security of data, and the holding the capsule's position during treatment, In conclusion, both methods have the potential to make capsule endoscopy a very patient friendly procedure that can be carried out anywhere.

This letter presents a performance evaluation of wireless communications applicable into a capsule endoscope. A numerical model to describe the received signal strength (RSS) radiated from a capsule-sized signal generator is derived through measurements in which a liquid phantom is used that has electrical constants equivalent to human tissue specified by IEC 62209-1. By introducing this model and taking into account the characteristics of its direction pattern of the capsule and propagation distance between the implanted capsule and on-body antenna, a cumulative distribution function (CDF) of the received SNR is evaluated. Then, simulation results related to the error ratio in the wireless channel are obtained. These results show that the frequencies of 611 MHz or lesser would be useful for the capsule endoscope applications from the view point of error rate performance. Further, we show that the use of antenna diversity brings additional gain to this application.

An implantable WBAN path-loss model for a capsule endoscopy which is used for examining digestive organs, is developed by conducting simulations and experiments. First, we performed FDTD simulations on implant WBAN propagation by using a numerical human model. Second, we performed FDTD simulations on a vessel that represents the human body. Third, we performed experiments using a vessel of the same dimensions as that used in the simulations. On the basis of the results of these simulations and experiments, we proposed the gradient and intercept parameters of the simple path-loss in-body propagation model.

Interpretations by physicians of capsule endoscopy image sequences captured over periods of 7-8 hours usually require 45 to 120 minutes of extreme concentration. This paper describes a novel method to reduce diagnostic time by automatically controlling the display frame rate. Unlike existing techniques, this method displays original images with no skipping of frames. The sequence can be played at a high frame rate in stable regions to save time. Then, in regions with rough changes, the speed is decreased to more conveniently ascertain suspicious findings. To realize such a system, cue information about the disparity of consecutive frames, including color similarity and motion displacements is extracted. A decision tree utilizes these features to classify the states of the image acquisitions. For each classified state, the delay time between frames is calculated by parametric functions. A scheme selecting the optimal parameters set determined from assessments by physicians is deployed. Experiments involved clinical evaluations to investigate the effectiveness of this method compared to a standard-view using an existing system. Results from logged action based analysis show that compared with an existing system the proposed method reduced diagnostic time to around 32.5 7 minutes per full sequence while the number of abnormalities found was similar. As well, physicians needed less effort because of the systems efficient operability. The results of the evaluations should convince physicians that they can safely use this method and obtain reduced diagnostic times.

In this study, we introduce a software pipeline to track feature points across endoscopic video frames. It deals with the common problems of low contrast and uneven illumination that afflict endoscopic imaging. In particular, irregular feature trajectories are eliminated to improve quality. The structure of soft tissue is determined by an iterative factorization method based on collection of tracked features. A shape updating mechanism is proposed in order to yield scale-invariant structures. Experimental results show that the tracking method produced good tracking performance and increased the number of tracked feature trajectories. The real scale and structure of the target scene was successfully estimated, and the recovered structure is more accuracy than the conventional method.

Gastric cancer is a great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper, for the early detection of gastric cancer, proposes the analysis system of an endoscopic image of the stomach, which detects abnormal regions by using the change of color in the image and by providing the surface tissue information to the detector. While advanced inflammation or cancer may be easily detected, early inflammation or cancer is difficult to detect and requires more attention to be detected. This paper, at first, converts an endoscopic image to an image of the IHb (Index of Hemoglobin) model and removes noises incurred by illumination and, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of abnormal regions detected as gastric cancer, but it intends to provide a supplementary mean to reduce the load and mistaken diagnosis of the detector, by automatically detecting the abnormal regions not easily detected by the human eye and this provides additional information for diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic images of the stomach.