In this paper, we propose a diagnosis and classification method of hydrocephalus computed tomography (CT) images using deep learning and image reconstruction methods. The proposed method constructs pathological features differing from the other healthy tissues. This method tries to improve the accuracy of pathological images identification and diagnosis. Identification of pathological features from CT images is an essential subject for the diagnosis and treatment of diseases. However, it is difficult to accurately distinguish pathological features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions, etc. Some study results reported that the ResNet network has a better classification and diagnosis performance than other methods, and it has broad application prospectives in the identification of CT images. We use an improved ResNet network as a classification model with our proposed image reconstruction and information fusion methods. First, we evaluate a classification experiment using the hydrocephalus CT image datasets. Through the comparative experiments, we found that gradient features play an important role in the classification of hydrocephalus CT images. The classification effect of CT images with small information entropy is excellent in the evaluation of hydrocephalus CT images. A reconstructed image containing two channels of gradient features and one channel of LBP features is very effective in classification. Second, we apply our proposed method in classification experiments on CT images of colonography polyps for an evaluation. The experimental results have consistency with the hydrocephalus classification evaluation. It shows that the method is universal and suitable for classification of CT images in these two applications for the diagnosis of diseases. The original features of CT images are not ideal characteristics in classification, and the reconstructed image and information fusion methods have a great effect on CT images classification for pathological diagnosis.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Bouadjenek N, Nemmour H, Chibani Y (2015) Age, gender and handedness prediction from handwriting using gradient features. In: 13th International Conference on Document Analysis and Recognition, ICDAR 2015, Nancy, France, 23–26 August 2015, pp 1116–1120
Chen S, Yang H, Fu J, Mei W, Ren S, Liu Y, Zhu Z, Liu L, Li H, Chen H (2019) U-net plus: deep semantic segmentation for esophagus and esophageal cancer in computed tomography images. IEEE Access 7:82867–82877
Deng S, Zhang J, Li P, Huang G (2011) Edge detection from polarimetric SAR images using polarimetric whitening filter. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada, 24–29 July 2011 pp 448–451
Gadermayr M, Wimmer G, Uhl A, Kogler H, Vécsei A, Merhof D (2017) Fully-automated CNN-based computer aided celiac disease diagnosis. In: Image Analysis and Processing—ICIAP 2017—19th International Conference, Catania, Italy, 11–15 September 2017, Proceedings, Part II, pp 467–478
Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed 138:49–56
Godkhindi AM, Gowda RM (2017) Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis, pp 1722–1728
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp 770–778
Huo W, Huang Y, Pei J, Zhang Q, Gu Q, Yang J (2018) Ship detection from ocean SAR image based on local contrast variance weighted information entropy. Sensors 18(4):1196
Jabri S, Saidallah M, el Belrhiti el Alaoui A, Fergougui AE (2018) Moving vehicle detection using haar-like, LBP and a machine learning adaboost algorithm. In: IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018, Sophia Antipolis, France, 12–14 December 2018, pp 121–124
Kemaev I, Polykovskiy D, Vetrov DP (2018) Reset: learning recurrent dynamic routing in resnet-like neural networks. In: Proceedings of The 10th Asian Conference on Machine Learning, ACML 2018, Beijing, China, 14–16 November 2018, pp 422–437
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3–6, 2012, Lake Tahoe, Nevada, United States, pp 1106–1114
Li Y, Zhang L (2015) Feature fusion of gradient direction and LBP for facial expression recognition. In: Biometric Recognition—10th Chinese Conference, CCBR 2015, Tianjin, China, 13–15 November 2015, Proceedings, pp 423–430
Liu X, Hou F, Qin H, Hao A (2018) Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recognit 77:262–275
Luo X, Wu X, Zhang Z (2014) Regional and entropy component analysis based remote sensing images fusion. J Intell Fuzzy Syst 26(3):1279–1287
Ma B, Liu Z, Jiang F, Yan Y, Yuan J, Bu S (2019) Vehicle detection in aerial images using rotation-invariant cascaded forest. IEEE Access 7:59613–59623
Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB, Yee J, Dey J, Napel S (2004) Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical ct. IEEE Trans Med Imaging 23(6):661–675
Pei Y (2019) Automatic decision making for parameters in kernel method. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 3207–3214
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp 1–9
Varma P, Iter D, Sa CD, Ré C (2017) Flipper: a systematic approach to debugging training sets. In: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, HILDA@SIGMOD 2017, Chicago, IL, USA, 14 May 2017, pp 5:1–5:5
Chen W, Smith R, Ji SY (2009) Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Mak 9(1):S1–S4
Xiao K, Ho SH, Salih Q (2007) Segmentation of lateral ventricles in brain MRI using fuzzy c-means clustering with Gaussian smoothing. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp 161–170
Zhang A, Kao PY, Sahyouni R, Shelat A, Chen J, Manjunath B (2019) Automated segmentation of ct scans for normal pressure hydrocephalus. arXiv preprint arXiv:1901.09088
Zhang Y, Song Z (2007) Automated detection of colon polyps for CT colonography. Chin J Biomed Eng 26(2):226–230
Zhong X, Feng G, Wang J, Wang W, Si W (2015) A novel adaptive image zooming scheme via weighted least-squares estimation. Front Comput Sci 9(5):703–712
This work was supported in part by the Beijing Municipal Science and Technology Project (No. KM201910005028).
Conflict of interest
The authors declare that they have no conflicts of interest.
All procedures performed in studies 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.
Informed consent was obtained from all individual participants involved in the study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Li, P., Li, J., Chen, Y. et al. Classification and recognition of computed tomography images using image reconstruction and information fusion methods. J Supercomput 77, 2645–2666 (2021). https://doi.org/10.1007/s11227-020-03367-y
- Computed tomography images
- Image reconstruction
- Information fusion
- Deep learning