Segmenting Reddish Lesions in Capsule Endoscopy Images Using a Gastrointestinal Color Space

Vu, Hai; Echigo, Tomio; Imura, Yoshinori; Yanagawa, Yukiko; Yagi, Yasushi

doi:10.1109/icpr.2014.562

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Several methods have been proposed in the literature for automated segmentation of lesions in endoscopic images. Vu et al implemented an automated segmentation method for detecting lesions using local mean image measurements along with two-level thresholding [10]. Individual color features such as red (R), green(G), and blue (B) and RGB triplet are extracted from the gaussian lab (GL) color space.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

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Section: Introductionmentioning

confidence: 99%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Novel Adaptive Statistical Method-CNN Synergism Based Two-Step WCE Image Segmentation

Mandal

Adhikari

Banerjee

et al. 2019

2019 International Conference on Intelligent Computing and Control Systems (ICCS)

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DCS-UNet: Dual-Path Framework for Segmentation of Reflux Esophagitis Lesions from Endoscopic Images with U-Net-Based Segmentation and Color/Texture Analysis

Tran

et al. 2022

Vietnam J. Comp. Sci.

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Automatic analysis of medical images and endoscopic images, in particular, is an attractive research topic in recent years. To achieve this goal, many tasks must be conducted for example lesion detection, segmentation, and classification. However, existing methods for such problems still face challenges due to the spreading characteristic of the lesions as well as some artifacts caused by motion, specularities, low contrast, bubbles, debris, bodily fluid, and blood. As a consequence, single segmentation or detection could not deal with those issues. Besides, although deep learning has achieved impressive results in many tasks, lacking a large annotated dataset could lead to overfitting problems. In this paper, we tackle these issues by taking particular characteristics of lesions of interest and advantages of deep segmentation models into account. We propose a dual-path framework (namely, DCS-UNet) that combines both segmentation and classification. We first segment lesions from the image using U-Net architecture with different encoder backbones (ResNet-50, VGG-16, DenseNet-201). The segmented regions will be then refined in the second path where we classify every patch in the whole image or the Inner or Outer regions extended from the contours given by the segmentation results. For the refinement scheme, we utilize the color-dependent and texture features as doctors’ advice and deploy the support vector machine (SVM) technique to classify a patch into disease region or not. Extensive experiments were conducted on three datasets of gastroesophageal reflux disease (GERD) endoscopic images as GradeM, GradeA, GradeB, which are defined as modified Los Angeles classification. The experimental results show the improvements of the proposed method compared to the single U-Net or a segmentation using the hand-designed features-based scheme. The proposed method improves mDice and mIoU by 0.5% and 0.36% on GradeA dataset, which is the most challenging dataset with ambiguous separations of GERD vs. normal regions, and by 0.82% and 0.81% on GradeM dataset, which is clearer to segment GERD regions. This proposed framework shows the potential to develop a diagnosis assistance system to help endoscopists reduce burden in examining GERD in the future.

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Segmenting Reddish Lesions in Capsule Endoscopy Images Using a Gastrointestinal Color Space

Cited by 3 publications

References 14 publications

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Novel Adaptive Statistical Method-CNN Synergism Based Two-Step WCE Image Segmentation

DCS-UNet: Dual-Path Framework for Segmentation of Reflux Esophagitis Lesions from Endoscopic Images with U-Net-Based Segmentation and Color/Texture Analysis

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