Local fractal dimension based approaches for colonic polyp classification

Häfner, Michael; Tamaki, Toru; Tanaka, Shinji; Uhl, Andreas; Wimmer, Georg; Yoshida, Shigeto

doi:10.1016/j.media.2015.08.007

Cited by 50 publications

(33 citation statements)

References 29 publications

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“…Despite the difference between natural and medical images, some feature descriptors designed especially for natural images are used successfully in medical image detection and classification, for example, texture-based polyp detection [3], Fourier and Wavelet filters for colon classification [18], shape descriptors [44], and local fractal dimension [45] for colonic polyp classification. Additionally, recent studies show the potential of the knowledge transfer between natural and medical images using pretrained (off-the-shelf) CNNs [34, 46].…”

Section: Methodsmentioning

confidence: 99%

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Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

Self Cite

145

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Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

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Section: Methodsmentioning

confidence: 99%

“…The Blob Shape adapted Gradient using Local Fractal Dimension method combines BA-LFD features with shape and contrast histograms from the original and gradient image [45]. …”

Section: Methodsmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

Self Cite

145

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“…showed that CAD had an accuracy of 93.1%, which was superior to that of non‐experts and supporting the hypothesis that CAD could be a powerful support for novice endoscopists. Following these studies, a research group at Hiroshima University in Japan played a significant role in the development of CAD models . Unlike the previous studies, they adopted a histogram of visual words to the algorithm to make a more robust system for image analysis.…”

Section: Automated Polyp Characterizationmentioning

confidence: 99%

“…Following these studies, a research group at Hiroshima University in Japan played a significant role in the development of CAD models. [29][30][31][32][33][34][35] Unlike the previous studies, they adopted a histogram of visual words to the algorithm to make a more robust system for image analysis. Their achievement was notable because they realized real-time prediction of polyp pathology.…”

Section: Magnifying Narrow-band Imagingmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

119

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Background and Aim Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.

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“…33 For the classification of colonic polyps, a wide variety of CAD systems have been proposed. These systems typically exploit the pit-patterns identified by Kudo et al, 34 to develop discriminative features for the analysis of magnifying endoscopy in combination with narrow band imaging [35][36][37][38][39] or iScan. 39 Although 2 of these studies reported a diagnostic concordance between the CAD system and the experts exceeding 97%, a 2010 study showed that human observers still demonstrate a superior performance.…”

Section: Current Status and Future Applications Of Cad In Endoscopymentioning

confidence: 99%

Novel Developments in Endoscopic Mucosal Imaging

2018

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Endoscopic techniques such as high-definition and optical-chromoendoscopy have had enormous impact on endoscopy practice. Since these techniques allow assessment of most subtle morphological mucosal abnormalities, further improvements in endoscopic practice lay in increasing the detection efficacy of endoscopists. Several new developments could assist in this. First, web based training tools could improve the skills of the endoscopist for enhancing the detection and classification of lesions. Secondly, incorporation of computer aided detection will be the next step to raise endoscopic quality of the captured data. These systems will aid the endoscopist in interpreting the increasing amount of visual information in endoscopic images providing real-time objective second reading. In addition, developments in the field of molecular imaging open opportunities to add functional imaging data, visualizing biological parameters, of the gastrointestinal tract to white-light morphology imaging. For the successful implementation of abovementioned techniques, a true multi-disciplinary approach is of vital importance.

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Local fractal dimension based approaches for colonic polyp classification

Cited by 50 publications

References 29 publications

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Artificial intelligence and colonoscopy: Current status and future perspectives

Novel Developments in Endoscopic Mucosal Imaging

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