In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)- an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
The Endoscopic Video data used to support the findings of this study are available from following websites:
Endoscopic videos collected from Lab Aid Hospital, Dhaka can be available upon request to the corresponding author.
Abouelenien M, Yuan X, Giritharan B, Liu J, Tang S (2013) Cluster-based sampling and ensemble for bleeding detection in capsule endoscopy videos. American Journal of Science and Engineering 2(1):24–32
Alexandre LA, Nobre N, Casteleiro J (2008) Color and position versus texture features for endoscopic polyp detection. In: International conference on biomedical engineering and informatics, 2008. BMEI 2008, vol 2. IEEE, pp 38–42
Bernal J, Tajkbaksh N, Sánchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I, et al. (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. IEEE Trans Med Imaging 36(6):1231–1249
Berrendero JR, Cuevas A, Torrecilla JL (2016) The mrmr variable selection method: a comparative study for functional data. J Stat Comput Simul 86(5):891–907
Billah M, Waheed S (2018) Gastrointestinal polyp detection in endoscopic images using an improved feature extraction method. Biomed Eng Lett 8(1):69–75
Billah M, Waheed S, Rahman MM (2017) An automatic gastrointestinal polyp detection system in video endoscopy using fusion of color wavelet and convolutional neural network features. Int J Biomed Imaging 2017
Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(02):185–205
Farah FMBD, Wahid KA (2020) Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomedical Signal Processing and Control 55:101530
Girshick TDR, Donahue J, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation CVPR
Iakovidis DK, Maroulis DE, Karkanis SA (2006) An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy. Comput Biol Med 36(10):1084–1103
Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M (2003) Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomedicine 7(3):141–152
Kodogiannis V, Boulougoura M (2007) An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging. Int J Inf Technol 13(1):46–56
Kopelman OGHJPSY, Cohen A (2019) Automated polyp detection system in colonoscopy using deep learning and image processing techniques. Journal of Gastroenterology and its Complications 3(1):101
Li B, Fan Y, Meng MQ-H, Qi L (2009) Intestinal polyp recognition in capsule endoscopy images using color and shape features. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 1490–1494
Li B, Qi L, Meng MQ-H, Fan Y (2009) Using ensemble classifier for small bowel ulcer detection in wireless capsule endoscopy images. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 2326–2331
Liu DECSSRCYFW, Anguelov D, Berg AC (2016) Ssd: single shot multibox detector ECCV
Mehmood R, Shahzad W, Ahmed E (2017) Maximum relevancy minimum redundancy based feature subset selection using ant colony optimization. J Appl Environ Biol Sci 7(4):118–130
Mesejo P, Pizarro D, Abergel A, Rouquette O, Beorchia S, Poincloux L, Bartoli A (2016) Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Trans Medical Imaging 35(9):2051–2063
Mingjian XZGQMZHDLMS, Qu Y (2019) Automatic polyp detection in colonoscopy images: convolutional neural network, dataset and transfer learning. Journal of Medical Imaging and Health Informatics 9(1):126–133
Nagito ATJHS, Shibata T (2019) Automated detection of fundic gland polyps from endoscopic images using ssd. International Society for Optics and Photonics 11049:110492O
Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238
Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics 18(1):9
Redmon J, Farhadi A (2016) Yolo9000: better, faster, stronger. arXiv:1612.08242
Ribeiro E, Uhl A, Häfner M (2016) Colonic polyp classification with convolutional neural networks. In: 2016 IEEE 29th international symposium on computer-based medical systems (CBMS). IEEE, pp 253–258
Shen H-B, Chou K-C (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics 22(14):1717–1722
Sudhir FMS, Yi S (2019) Region-based automated localization of colonoscopy and wireless capsule endoscopy polyps. Appl Sci 9(12):2404
Unler A, Murat A, Chinnam RB (2011) Mr2pso: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf Sci 181(20):4625–4641
Vergara JR, Estévez PA (2014) A review of feature selection methods based on mutual information. Neural computing and applications 24(1):175–186
Zhu R, Zhang R, Xue D (2015) Lesion detection of endoscopy images based on convolutional neural network features. In: 2015 8th international congress on image and signal processing (CISP). IEEE, pp 372–376
Zou Y, Li L, Wang Y, Yu J, Li Y, Deng WJ (2015) Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. In: 2015 IEEE international conference on digital signal processing (DSP). IEEE, pp 1274–1278
The authors are very grateful to Dr. Md. Rabiul Hossain, Gastroenterologist, Liver & Internal Medicine Specialist, Labaid Hospital, Dhaka for his valuable support, suggestions, and consultancy.
All the research work in this paper has been conducted by self funding.
All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Conflict of interests
The authors declare that, there is no conflict of interest regarding this paper.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Billah, M., Waheed, S. Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09151-7
- Minimum redundancy maximum relevance (mRMR)
- Video endoscopy
- Ensemble classifier
- Feature selection
- Convolutional Neural Network (CNN)
- Color Wavelet (CW)
- Feature extraction