scholarly journals An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1617
Author(s):  
Ioannis Intzes ◽  
Hongying Meng ◽  
John Cosmas
Keyword(s):  
Power Consumption ◽  
Capsule Endoscopy ◽  
State Of The Art ◽  
Logic Gates ◽  
Low Complexity ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Huffman Coding ◽  
Medical Diagnoses ◽  
Wireless Capsule

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.

scholarly journals Multi-scale Features Fusion for the Detection of Tiny Bleeding in Wireless Capsule Endoscopy Images

2022 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Feng Lu ◽  
Wei Li ◽  
Song Lin ◽  
Chengwangli Peng ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
State Of The Art ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Feature Representation ◽  
Common Symptom ◽  
Content Type ◽  
Multi Scale ◽  
Wireless Capsule

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

Study on the Design of a Magnetic Started Wireless Capsule Endoscopy

2012 ◽  
Vol 195-196 ◽  
pp. 864-867
Author(s):  
Ya Zhen Wang ◽  
Ying Jun Chen ◽  
Huang Ping
Keyword(s):  
Power Consumption ◽  
System Design ◽  
Capsule Endoscopy ◽  
Optical Design ◽  
Image Sensor ◽  
Wireless Capsule Endoscopy ◽  
Low Power Consumption ◽  
Total Size ◽  
In Series ◽  
Wireless Capsule

Based on the minimal CMOS image sensor OV6920, a magnetic controlled wireless capsule endoscopy has been designed in principles of micromation and low power consumption. The peripheral circuit of OV6920 is designed. With optical design, the power consumption of transmitting circuit is cut down according to the relationships between the resistance in series and the voltage, current, and transmitting power consumption. The total size of the system is only φ13×29mm, and the power consumption is about 100mW when all modules are connected altogether. The clear captured images in the imaging experiments prove the system design is feasible.

scholarly journals High Data Rate FinFET On-Off Keying Transmitter for Wireless Capsule Endoscopy

VLSI Design ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Ioannis Intzes ◽  
Hongying Meng ◽  
John P. Cosmas
Keyword(s):  
Power Consumption ◽  
Capsule Endoscopy ◽  
Average Power ◽  
Endoscopic Examination ◽  
Data Rate ◽  
Wireless Capsule Endoscopy ◽  
High Data ◽  
Rate Transmission ◽  
Average Power Consumption ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a painless diagnostic tool used by the physicians for endoscopic examination of the gastrointestinal track. The performance of the existing WCE systems is limited by high power consumption and low data rate transmission. In this paper, a 144 MHz FinFET On-Off Keying (OOK) transmitter is designed and integrated with a class-E power amplifier. It is implemented and simulated using 16 nm FinFET Predictive Technology Models. The proposed transmitter can achieve the data rate of 33 Mbps with average power consumption of 1.04 mW from a 0.85 V power supply in the simulation. This design outperforms the current state-of-the-art designs.

scholarly journals Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132850-132859 ◽  
Author(s):  
Muhammad Attique Khan ◽  
Seifedine Kadry ◽  
Majed Alhaisoni ◽  
Yunyoung Nam ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Features Selection ◽  
Computer Aided ◽  
Wireless Capsule

Wireless capsule endoscopy detects small bowel ulcers in patients with normal results from state of the art enteroclysis

2003 ◽  
Vol 98 (6) ◽  
pp. 1295-1298 ◽  
Author(s):  
Suthat Liangpunsakul ◽  
Vidyasree Chadalawada ◽  
Douglas K. Rex ◽  
Dean Maglinte ◽  
John Lappas
Keyword(s):  
Small Bowel ◽  
Capsule Endoscopy ◽  
State Of The Art ◽  
Wireless Capsule Endoscopy ◽  
Wireless Capsule

scholarly journals Prelimenary results of red lesion segmentation in WCE images

2021 ◽  
Vol 297 ◽  
pp. 01060
Author(s):  
Said Charfi ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani ◽  
Ilyas El Jaafari
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Imaging Technique ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Gastrointestinal Illness ◽  
Lesion Segmentation ◽  
Entire Small Bowel ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.

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