An Efficient Cancer Classification Model for CT/MRI/PET Fused Images

Author(s): S. Srimathi*, G. Yamuna, R. Nanmaran

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 17 , Issue 3 , 2021

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Objective: The aim was to study image fusion-based cancer classification models used to diagnose cancer and assess medical problems in earlier stages that help doctors or health care professionals to make the treatment plan accordingly.

Methods: In this work, a novel image fusion method based on Curvelet transform is developed. CT and PET scan images of benign type tumors were fused together using the proposed fusion algorithm and the same way, MRI and PET scan images of malignant type tumors were fused together to achieve the combined benefits of individual imaging techniques. Then, the marker-controlled watershed algorithm was applied on fused images to segment cancer affected area. The various color features, shape features and texture-based features were extracted from the segmented image. Following this, a data set was formed with various features, given as input to different classifiers namely neural network classifier, Random forest classifier, and K-NN classifier to determine the nature of cancer. The results of the classifier showed normal, benign or malignant category of cancer.

Results: The performance of the proposed fusion algorithm was compared with the existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of the classifiers were evaluated using three parameters: accuracy, sensitivity, and specificity. The K-NN Classifier performed better compared to the other two classifiers and it provided an overall accuracy of 94%, sensitivity of 88% and specificity of 84%.

Conclusion: The proposed Curvelet transform based image fusion method combined with the KNN classifier provides better results compared to other two classifiers when two input images were used individually.

Keywords: Curvelet transform, marker controlled watershed algorithm, neural network classifier, random forest classifier, K-nearest neighbour classifier, CT, PET.

Srimathi S, Yamuna G. Study of cancer detection techniques using various image processing algorithms. Int J Eng Dev Res 2018; 6: 341-6.
Rastogi T, Hildesheim A, Sinha R. Opportunities for cancer epidemiology in developing countries. Nat Rev Cancer 2004; 4(11): 909-17.
[] [PMID: 15516963]
Starck JL, Candès EJ, Donoho DL. The curvelet transform for image denoising. IEEE Trans Image Process 2002; 11(6): 670-84.
[] [PMID: 18244665]
Das S, Chowdhury M, Kundu MK. Medical image fusion based on ripplet transform type-I. Prog Electromagn Res B 2011; 30: 355-70.
Radha N, Babu TR. Stationary wavelet transform based image fusion using fusion rules. Int J Eng Adv Technol 2019; 9: 3045-8.
Koteswararao K, Swamy KV. Multimodal medical image fusion using NSCT and DWT fusion frame work. Int J Innov Technol Explor Eng 2019; 9: 3643-8.
Budhewar ST. Wavelet and curvelet transform based image fusion algorithm. Int J Innov Technol 2014; 5: 3703-7.
Guo P, Lin W, Li L, Zeng Y, Sun T. Image fusion and classification of ENVISAT ASAR and ETM+ data. 2014 7th International Congress on Image and Signal Processing Dalian, China. 563-8.
Saranya C, Shoba S. Comparison of image fusion technique by various transform based methods. Int J Engine Res 2015; 4: 258-62.
Vinay Sahu DS. Image fusion using wavelet transform: A review. Glob J Comput Sci Technol 2014; 3: 66-9.
Bharath B. A comprehensive survey of multimodal image fusion schemes. Int J Recent Technol Eng 2018; 4: 2277-3878.
Harinkhede S, Mishra MS. A comparatively analysis of various hybrid image fusion techniques. Int J Eng Sci Res Technol 2018; 7: 51-5.
Li C, Xu D. Study on methods of fusion and classification using SPOT5 image of ZhongShan cemetery. 2009. Jt Urban Remote Sens Event 2009; 3: 36-44.
Du P, Yuan L, Xia J, He J. Fusion and classification of Beijing-1 small satellite remote sensing image for land cover monitoring in mining area. Chin Geogr Sci 2011; 21: 656-65.
Mule MB. Basic medical image fusion methods. Int J Adv Res Comput Eng Technol 2015; 4: 1046-9.
Bahl M, Kaur H. Image fusion using wavelet and curvelet transforms. Int J Adv Eng Res Dev 2015; 2: 2467-70.
Wei T, Gao Q, Ma N, et al. Feature-level image fusion through consistent region segmentation and dual-tree complex wavelet transform. J Imaging Sci Technol 2016; 60: 1-11.
Tan O, Jia C, Duan H, Lu W, Atlas B, Factor S. Transform based fusion methods. 2014; 3545: 508-11.
Srimathi S, Yamuna G, Nanmaran R. Neural networks based cancer classification model using CT-PET fused images. Adv Comput Data Sci 2019; 1045: 104-16.
Tian H, Wang PG, Zheng W. A new image fusion algorithm based on fractional wavelet transform. Proc 2nd Int Conf Comput Sci Netw Technol. 2175-8.
Suthakar J. Study of image fusion-techniques, method and applications. Int J Comput Sci Mob Comput 2014; 3: 469-76.
Yeo H, Sheinin V, Sheinin Y. An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei. Med Imaging 2009: Image Process 2009; 7259: 725948.
Duta N, Sonka M. Segmentation and interpretation of MR brain images: an improved active shape model. IEEE Trans Med Imaging 1998; 17(6): 1049-62.
[] [PMID: 10048862]
Nie D, Shank EA, Jojic V. A deep framework for bacterial image segmentation and classification. BCB 2015 - 6th ACM Conf Bioinformatics, Comput Biol Heal Informatics. 5: 306-14.
Ravikumar S. Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine. Artif Cells Nanomed Biotechnol 2016; 44(3): 985-9.
[PMID: 25707440]
Arunachalam HB, Mishra R, Armaselu B, et al. Computer-aided image segmentation and classification for viable and non-viable tumor identification in osteosarcoma. Pac Symp Biocomput 2017; 22: 195-206.
[] [PMID: 27896975]
Gao Z, Wu Y, Bao Y, Gong J, Wang J, Rohani S. Image analysis for in-line measurement of multidimensional size, shape, and polymorphic transformation of l -glutamic acid using deep learning-based image segmentation and classification. Cryst Growth Des 2018; 18: 4275-81.
Hsiao YT, Chuang CL, Lu YL, Jiang JA. Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames. Image Vis Comput 2006; 2: 1123-36.
Coombes M, Eaton W, Chen WH. Colour based semantic image segmentation and classification for unmanned ground operations. 2016 Int Conf Unmanned Aircr Syst ICUAS 2016. 858-67.
Pitchumani Angayarkanni S, Kamal NB. Mathematical morphological approach for mammogram image segmentation and classification. World Appl Sci J 2014; 31: 1056-64.
Padma A, Giridharan N. Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images. Int J Imaging Syst Technol 2016; 26: 97-105.
Igual L, Soliva JC, Escalera S, Gimeno R, Vilarroya O, Radeva P. Automatic brain caudate nuclei segmentation and classification in diagnostic of attention-deficit/hyperactivity disorder. Comput Med Imaging Graph 2012; 36(8): 591-600.
[] [PMID: 22959658]
Xu Y, Zhu JY, Chang EIC, Lai M, Tu Z. Weakly supervised histopathology cancer image segmentation and classification. Med Image Anal 2014; 18(3): 591-604.
[] [PMID: 24637156]
Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ. Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inform 2015; 19(1): 377-88.
[] [PMID: 25561457]
Richard N, Fernandez-Maloigne C, Bonanomi C, Rizzi A. Fuzzy color image segmentation using watershed transform. J Image Graph 2013; 5: 157-60.
Verma A. The marker-based watershed segmentation- a review. Int J Eng Innov Technol 2013; 3: 171-4.
Chen HM, Tsao YT, Tsai SN. Automatic image segmentation and classification based on direction texton technique for hemolytic anemia in thin blood smears. Mach Vis Appl 2014; 25: 501-10.
Abkar AA, Sharifi MA, Mulder NJ. Likelihood-based image segmentation and classification: A framework for the integration of expert knowledge in image classification procedures. Int J Appl Earth Obs Geoinf 2000; 4: 104-19.
Yu P, Qin AK, Clausi DA. Unsupervised polarimetric SAR image segmentation using region growing with edge penalty. IEEE Trans Geosci Remote Sens 2012; 50: 1302-17.
Farmer ME, Jain AK. A wrapper-based approach to image segmentation and classification. IEEE Trans Image Process 2005; 14(12): 2060-72.
[] [PMID: 16370459]
Li Y, Shen L, Yu S. HEp-2 Specimen image segmentation and classification using very deep fully convolutional network. IEEE Trans Med Imaging 2017; 36(7): 1561-72.
[] [PMID: 28237925]
Rouhi R. A Review on Feature Extraction Techniques in Face Recognition. Int J Signal Image Process 2012; 3: 1-14.
Ali Mustafa R, Thabt Saleh K, Salman Chyad H. Feature extraction based on wavelet transform and moment invariants for medical image. Int J Eng Res Adv Technol 2018; 4: 80-98.
Zhou XS, Cohen I, Tian Q, Huang TS. Feature extraction and selection for image retrieval. 2001; 3: 1-7.
Choras RS. Image feature extraction techniques and their applications for CBIR and biometrics systems. Int J Biol Biomed Eng 2007; 1: 6-15.
Eker O, Seker DZ. Semi-automatic extraction of features from digital imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 443-6.
Krishna Kumar NJ, Balakrishna R. EEG feature extraction using daubechieswavelet and classification using neural network. Int J Comput Sci Eng 2019; 7: 792-9.
Asogwa TC, Fidelis E, Obodoeze C, Obiokafor IN. Wireless Sensor Network (WSN): Applications in oil & gas and agriculture industries in Nigeria. Int J Adv Res Comput Commun Eng ISO 2007; 3297: 153-5.
Vardhan MH, Rao SV. GLCM architecture for image extraction. Int J Adv Res Electron Commun Eng 2014; 3: 75-82.
Sahu M, Saxena A, Manoria M. Application of feature extraction technique: a review. Int J Comput Sci Inf Technol 2015; 6: 3014-6.
Shijin Kumar PS, Dharun V. Extraction of texture features using GLCM and shape features using connected regions. Int J Eng Technol 2016; 8: 2926-30.
Ahmed Medjahed S. A comparative study of feature extraction methods in images classification. Int J Image Graph Signal Process 2015; 7: 16-23.
Mohanaiah P, Sathyanarayana P, Gurukumar L. Image texture feature extraction using GLCM approach. Int J Sci Res Publ 2013; 3: 1-5.
Kumar G, Bhatia PK. A detailed review of feature extraction in image processing systems. Int Conf Adv Comput Commun Technol 3: 5-12.
Kunaver M, Tasič JF. Image feature extraction - An overview. EUROCON 2005 - The International Conference on "Computer as a Tool" 3: 183-6.
Bagri N, Johari PK. A comparative study on feature extraction using texture and shape for content based image retrieval. Int J Adv Sci Technol 2015; 80: 41-52.
Thamaraichelvi B, Yamuna G. Gray level co-occurrence Matrix features based classification of tumor in medical images. ARPN J Eng Appl Sci 2016; 11: 11403-14.
Mshari YT, Younis HA. Content based image retrieval using haar wavelet to extracted color histogram and texture features. Int J Comput Sci Mobile Comput 2015; 4: 322-9.
Zhao M, Chai Q, Zhang S. A method of image feature extraction using wavelet transforms. Int Conf Intell Comput 2009; 5754: 187-92.
Seetha M, Muralikrishna I, Deekshatulu B, Malleswari BL, Hegde P. Artificial neural networks and other methods of image classification. Theor Appl Inf Technol 2008; 4: 1039-53.
Sha DD, Sutton JP. Towards automated enhancement, segmentation and classification of digital brain images using networks of networks. Inf Sci (Ny) 2001; 138: 45-77.
Zhang XQ, Zhao SG. Cervical image classification based on image segmentation preprocessing and a CapsNet network model. Int J Imaging Syst Technol 2019; 29: 19-28.
Cruz DA, Villar-Patiño C, Guevara E, Martinez-Alanis M. Cervix type classification using convolutional neural networks. IFMBE Proc 2020; 75: 377-84.
Layek K, Das S, Samanta S. DWT based sonoelastography prostate cancer image classification using back propagation neural network. Proc - 2016 2nd IEEE Int Conf Res Comput Intell Commun Networks. 5: 66-71.
Kassani SH, Kassani PH, Wesolowski MJ, Schneider KA, Deters R. Classification of histopathological biopsy images using ensemble of deep learning networks. Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering 2019; 75: 377-84.
Akar Ö, Güngör O. Classification of multispectral images using Random Forest algorithm. J Geod Geoinf 2012; 1: 105-12.
Saravanan K, Sasithra S. Review on classification based on artificial neural networks. Int J Ambient Syst Appl 2014; 2: 11-8.
Lowe B, Kulkarni A. Multispectral image analysis using random forest. Int J Soft Comput 2015; 6: 1-14.
Kumar N, Kumar D. Classification using artificial neural network optimized with bat algorithm. Int J Innov Technol Explor Eng 2020; 9: 696-700.
Gao X, Wen J, Zhang C. An improved random forest algorithm for predicting employee turnover. Math Probl Eng 2019; 1155: 40-7.
Horning N. Random forests: An algorithm for image classification and generation of continuous fields data sets. Int Conf Geoinformatics Spat Infrastruct Dev Earth Allied Sci. 1-6.
Okun O, Priisalu H. Random forest for gene expression based cancer classification: Overlooked issues.Pattern Recognit Image Analysis 2007; 4478: 483-90.
Amato G, Falchi F. KNN based image classification relying on local feature similarity. Proc - 3rd Int Conf SImilarity Search Appl. 36: 101-8.
Charde PA, Lokhande SD. Classification using K nearest neighbor for brain image retrieval. Int J Sci Eng Res 2013; 4: 760-5.
Naveen Raj M, Aiswariya Lakshmi A, Edlin Shejila E, Kausalya K, Vinitha R. Lung image segmentation using modified k-means algorithm. Int J Innov Technol Explor Eng 2019; 8: 432-4.
Gaur R, Chouhan VS. Classifiers in image processing. Int J Futur Revolut Comput Sci Commun Eng IJFRCSCE 2017; 8: 22-4.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2021
Published on: 22 April, 2021
Page: [319 - 330]
Pages: 12
DOI: 10.2174/1573405616666200628134800
Price: $65

Article Metrics

PDF: 21