Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos

Hassan, Ahnaf Rashik; Haque, Mohammad Ariful

doi:10.1016/j.cmpb.2015.09.005

Cited by 98 publications

(51 citation statements)

References 23 publications

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“…The variation of NIG parameters calculated from TQWT sub-bands is further validated by the mean and standard deviation (SD) of ˛ and ı computed from each of the sub-bands presented in Table 1. As we can see from Table 1, the mean and SD values of the two NIG parameters are significantly different for all the sub-bands, confirming the usefulness of the two parameters for OSA classification [22]. Thus, we can conclude that NIG parameters in the TQWT domain differentiate normal and apneic sleep-ECG signals quite well.…”

Section: Efficacy Of Nig Parameters In the Tqwt Domainsupporting

confidence: 72%

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Hassan

2016

Biomedical Signal Processing and Control

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131

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Section: Efficacy Of Nig Parameters In the Tqwt Domainsupporting

confidence: 72%

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Hassan

2016

Biomedical Signal Processing and Control

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131

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“…A new method for bleeding detection using clustering based features was stated in [16]. In [18], a new feature descriptor that extracts texture feature using normalised GLCM (NGLCM) of the magnitude spectrum was proposed for detecting GI hemorrhage in WCE videos. Yuji Iwahori et al [22] proposed Hessian filter and histogram of gradients (HOG) features to distinguish between polyp and non-polyp regions, then used K-means++ for classification phase.…”

Section: Related Workmentioning

confidence: 99%

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Charfi

Ansari

Balasingham

2019

IET Image Processing

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Wireless capsule endoscopy (WCE) has revolutionized the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large amount of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reducing the diagnosis time and improve the detection accuracy. To address this problem, we propose a CAD system for automatic detection of ulcer in WCE images. Firstly, we enhance the input images to be better exploited in the main steps of the proposed method. Afterwards, segmentation using saliency map based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.

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“…Machine learning can reduce the computational cost of image processing and provide high accuracy; therefore, it is widely used in the detection of bleeding regions for endoscopy. (14)(15)(16)(17)(18)(19)(20)(21) The automation of hemostasis procedures is being studied because it is difficult to detect the hemostasis region; therefore, we have proposed a novel detection system (9) for hemostasis regions. In the present study, we analyzed the bleeding and hemostasis regions measured from endoscopic images using our detection method to clarify the termination conditions of our procedure in an automated hemostasis procedure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Hemostasis Procedures through Machine Learning of Endoscopic Images towards Automatic Surgery

Matsunaga¹,

Nakamura²

2020

Sensors and Materials

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Laparoscopic surgery reduces patient invasiveness; however, the burden on the surgeons is high because such surgery requires them to have skills higher than those for open procedures. In particular, improving the working environment of surgeons involves reducing the amount of human resources required and providing high-level medical services. The cooperation between robots and surgeons has been effective in the medical field; therefore, we focus on the automation of hemostasis procedures. An important factor in automation is target detection and the decision on the completion of the procedures. In this study, we analyzed hemostasis procedures by region detection through machine learning and developed a method of defining the termination conditions of the procedures. In hemostasis procedures, the bleeding region is coagulated by an energy device, the area of the hemostasis region increases, and the surgical procedure is continued. The method could detect the end of the procedures by monitoring the variations in the sizes of the bleeding and hemostasis regions.

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Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos

Cited by 98 publications

References 23 publications

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Analysis of Hemostasis Procedures through Machine Learning of Endoscopic Images towards Automatic Surgery

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