Automatic Segmentation and Detection of Small Bowel Angioectasias in WCE Images

Vieira, Pedro; Silva, Catarina P.; Costa, Dalila; Rolanda, Carla; Lima, C. S.

doi:10.1007/s10439-019-02248-7

Cited by 19 publications

(21 citation statements)

References 28 publications

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“…Several studies exist for abnormality detection in WCE images [9][10][11][12][13][14][15]. Most existing methods focus on identifying a specific lesion and few of them introduce a multilesion detection system for WCE images.…”

Section: Related Workmentioning

confidence: 99%

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

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Section: Related Workmentioning

confidence: 99%

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Amiri

Hassanpour

Beghdadi

2021

Journal of Healthcare Engineering

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“…The CMEMS-Uminho algorithm is the first to approach the transformation of the image according to the CIELab color system and to add a segmentation process. When compared to the methods already published to detect angioectasias in VCE static frames, the proposed system shows a better performance: sensitivity of 95.32%, specificity of 94.75% and ROC area of 97.87%[26]. Even though these results are promising, as in other algorithms previously described, it demands realistic experimentation.…”

Section: Introductionmentioning

confidence: 82%

“…The algorithm developed at the CMEMS-Uminho showed greater performance to detect angioectasias than other methods previously published [26]. To overcome the limitation inherent to most studies that examine CAD tools, we evaluated CMEMS-Uminho algorithm performance in full-length VCE recordings.…”

Section: Discussionmentioning

confidence: 99%

“…This occurs as a consequence of Mirocam ® frames displaying a more yellowish and reddish coloration, resulting in more false positives. Since the algorithm was trained with PillCam ® images [26], it possibly makes sense to change the segmentation method so that the CMEMS-Uminho algorithm can better separate the tissue from the angioectasias amongst the normal tissue. This can be done using a segmentation method that uses both channel a and channel b; or the inclusion of a normalization factor that takes into account the overall coloration of each image.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding color, angioectasias seem to have a better characterization in the CIELab than in the red, green, blue (RGB) color spaces [25]. In this line of thought, we worked with the Center for MicroElectroMechanics Systems (CMEMS-Uminho) to develop a novel algorithm to identify angioectasias in SB [26].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy

Costa

Vieira

Pinto

et al. 2020

GE Port J Gastroenterol

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Background: Video capsule endoscopy (VCE) revolutionized the diagnosis and management of obscure gastrointestinal bleeding, though the rate of detection of small bowel lesions by the physician is still disappointing. Our group developed a novel algorithm (CMEMS-Uminho) to automatically detect angioectasias which display greater accuracy in VCE static frames than other methods previously published. We aimed to evaluate the algorithm overall performance and assess its diagnostic yield and usability in clinical practice. Methods: Algorithm overall performance was determined using 54 full-length VCE recordings. To assess its diagnostic yield and usability in clinical practice, 38 VCE examinations with the clinical diagnosis of angioectasias consecutively performed (2017–2018) were evaluated by three physicians with different experiences. The CMEMS-Uminho algorithm was also applied. The performance of the CMEMS-Uminho algorithm was defined by a positive concordance between a frame automatically selected by the software and a study independent capsule endoscopist. Results: Overall performance in complete VCE recordings was 77.7%, and diagnostic yield was 94.7%. There were significant differences between physicians in regard to global detection rate (p < 0.001), detection rate per capsule (p < 0.001), diagnostic yield (p = 0.007), true positive rate (p < 0.001), time (p < 0.001), and speed viewing (p < 0.001). The application of CMEMS-Uminho algorithm significantly enhanced all readers’ global detection rate (p < 0.001) and the differences between them were no longer observed. Conclusion: The CMEMS-Uminho algorithm detained a good overall performance and was able to enhance physicians’ performance, suggesting a potential usability of this tool in clinical practice.

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Combination of Color-Based Segmentation, Markov Random Fields and Multilayer Perceptron

Vieira

Freitas

Rolanda

et al. 2021

Computer-Aided Analysis of Gastrointestinal Videos

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Angioectasias are lesions characterized by specific features, related to the color and shape. These lesions have a cherry red appearance, due to the nature of its origin (inflammation of blood vessels); and usually are characterized with a circular shape. As was previously reported, CIELab color has high efficiency in differentiating colors in an image (Connolly and Fleiss 1997). This color space is composed of three different channels: L represents the lightness information that goes from 0 (black) to 100 (diffuse white), and the components a and b represent the color-opponent dimensions. Negative values of channel a indicate green and positive indicate magenta (adequate for the detection of red color); and negative values of b indicate blue and positive indicate yellow (Weatherall and Coombs 1992). Since the red color in these lesions is easily spotted in the middle of the gastrointestinal tissue, CIELab was the chosen color space for processing the lesions. A pre-processing was included to overcome some problems with high values of the component a that do not represent a red area on the tissue. This step was designed by observation of a high number of images, leading to the understanding that certain areas of the images (usually near bubbles) could lead the segmentation step to fail. As was already shown in a previous work of the authors (Vieira et al. 2016), these types of lesions can be separated from the normal tissue by using probabilistic segmentation methods, as the Maximum A Posteriori (MAP) approach. This method was complemented with the use of Markov Random Fields (MRF) to improve the border definition. Because the segmentation

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Automatic Segmentation and Detection of Small Bowel Angioectasias in WCE Images

Cited by 19 publications

References 28 publications

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images

Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy

Combination of Color-Based Segmentation, Markov Random Fields and Multilayer Perceptron

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