Abstract
Detection and diagnosis of a disease with a single image can be tedious and difficult for doctors but with the adaptation of medical image fusion, a path for additional improvements can be paved. The objective of this research is to implement different fusion algorithms based on conventional and proposed hybrid techniques. Based on performance metrics it has been observed that the novel method, Discrete Component Wavelet Transform (DCWT) shows remarkable results in comparison to the traditional techniques. As per the enhancement methods, Binarization, Median Filter, and Contrast Stretching have been considered to compare the contrast performance with Contrast Limited Adaptive Histogram Equalization. Certain modifications to each enhancement method were made related to the selection of parameters. Thus, better qualitative and quantitative values were observed in Discrete Component Wavelet Transform. The different attributes were calculated from the fused images which were classified using various machine learning techniques. Maximum accuracy of 97.87% and 95.74% is obtained using Discrete Component Wavelet Transform for Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (k = 4) respectively considering the combination of both features Grey Level Difference Statistics and shape.
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Smeulders A W, Worring M, Santini S, Gupta A and Jain R 2000 Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22: 1349–1380
Eisenhauer E A, Therasse P, Bogaerts J, Schwartz L H, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D and Verweij J. 2009 New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45: 228–247
Vijan A, Dubey P and Jain S 2020 Comparative analysis of various image fusion techniques for brain magnetic resonance images. Proc. Comput. Sci. 167: 413–422
Dogra J, Jain S and Sood M 2019 Glioma extraction from MR images employing GBKS graph cut technique. Vis. Comput. 35(10): 1–17
Dogra J, Jain S and Sood M 2020 Gradeint based kernel selection technique for tumor detection and extraction from medical images using graph cut. IET Image Process. 14(1): 84–93
Lanaras C, Baltsavias E and Schindler K Estimating the relative spatial and spectral sensor response for hyperspectral and multispectral image fusion. https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/Papers/LanarasACRS16.pdf
Davis M E 2016 Glioblastoma: overview of disease and treatment. Clin. J. Oncol. Nurs. 20: S2
Arikan M, Fröhler B, and Möller T 2016 Semi-automatic brain tumor segmentation using support vector machines and interactive seed selection. In: Proceedings of the MICCAI-BRATS Workshop, pp. 1–3
Corso J J, Sharon E, Dube S, El-Saden S, Sinha U and Yuille A 2008 Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging 27: 629–640
Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition.Ch 55 ,https://www.ncbi.nlm.nih.gov/books/NBK378/
https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Cerebrovascular-Disease
Ambily P K, James S P and Mohan R R 2015 Brain tumor detection using image fusion and neural network. Int. J. Eng. Res. Gen. Sci. 3(2): 1383–1388
Wang M and Shang X 2020 A fast image fusion with discrete cosine transform. IEEE Signal Process. Lett. 27: 990–994
Kumar B K S, Swamy M N S and Ahmad M O 2013 Multiresolution DCT decomposition for multifocus image fusion. In: Proceedings of Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, Canada, pp. 1–4
Wang Z, Cui P, Li F, Chang E and Yang S 2014 A data-driven study of image feature extraction and fusion. Inform. Sci. 1–23
Snehkunj R, Jani A N and Jani N N 2018 Brain MRI/CT images feature extraction to enhance abnormalities quantification. Indian J. Sci. Technol. 11(1): 1–10
Sivakumar P, Velmurugan S P, and Sampson J 2020 Implementation of differential evolution algorithm to perform image fusion for identifying brain tumor. 3C Tecnología. In: Glosas de innovaciónaplicadas a la pyme. Edición Especial, Marzo, pp. 301–311
Maya A T, Suryono S and Anam C 2021 Image contrast improvement in image fusion between CT and MRI images of brain cancer patients. Int. J. Sci. Res. Sci. Technol. 8(1): 104–110
Masood S, Sharif M, Yasmin M, Shahidnd M A and Reh A 2017 Image fusion methods: a survey. J. Eng. Sci. Technol. Rev. 10(6): 187–191
Jain S, Sachdeva M, Dubey P and Vijan A 2019 Multi-sensor image fusion using intensity hue saturation technique. In: Luhach A, Jat D, Hawari K, Gao XZ., Lingras P (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1076. Springer, Singapore, pp. 147–157
Pal B, Mahajan S and Jain S 2020 Medical image fusion employing enhancement techniques. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India, pp. 223–226
Rajalingam B and Priya R 2017 A novel approach for multimodal medical image fusion using hybrid fusion algorithms for disease analysis. Int. J. Pure Appl. Math. 117(15): 599–619
Salau A O, Jain S and NnennaEneh J 2021 A review of various image fusion types and transform. Indonesian J. Electr. Eng. Comput. Sci. 24(3): 1515–1522
Li Y, Liu X, Wei F, Sima D M, Cauter S V, Himmelreich U, Pi Y, Hu G, Yao Y and Huffel S V 2017 An advanced MRI and MRSI data fusion scheme for enhancing unsupervised brain tumor differentiation. Comput. Biol. Med. 8(1): 121–129
Yong Y, Huang S, Gao J and Qian Z 2014 Multi-focus image fusion using an effective discrete wavelet transform based algorithm. Meas. Sci. Rev. 14(2): 102–108
Zitová B and Jan F 2003 Image registration methods: a survey. Image Vis. Comput. 21(11): 977–1000
Mohideen S K, Perumal S A and Sathik M M 2018 Image de-noising using discrete wavelet transform. IJCSNS Int. J. Comput. Sci. Netw. Sec. 8(1): 213–214
Pal B, Mahajan S and Jain S 2020 A comparative study of traditional image fusion techniques with a novel hybrid method. In: International Conference on Computational Performance Evaluation (ComPE) North-Eastern Hill University, Shillong, Meghalaya, India, pp. 820–825
Bhardwaj C, Jain S and Sood M 2019 Automatic blood vessel extraction of fundus images employing fuzzy approach. Indonesian J. Electr. Eng. Inform. 7(4): 757–771
Prashar N, Sood M and Jain S 2020 A novel cardiac arrhythmia processing using machine learning techniques. Int. J. Image Graph. 20(3): 2050023
Jain S 2018 Classification of protein kinase B using discrete wavelet transform. Int. J. Inf. Technol. 10(2): 211–216
Winarno A, Setiadi D R I M, Arrasyid A A, Sari C A and Rachmawanto E H 2017 Image watermarking using low wavelet subband based on 8×8 sub-block DCT. In: 2017 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, pp. 11–15
Salau A O and S Jain 2019 Feature extraction: a survey of the types, techniques and applications. In: 5th International Conference on Signal Processing and Communication (ICSC-2019), Jaypee Institute of Information Technology, Noida (INDIA), pp. 158–164
Sharma S, Jain S and Bhusri S 2017 Two class classification of breast lesions using statistical and transform domain features. J. Glob. Pharma Technol. 9(7): 18–24
Bhusri S, Jain S and Virmani J 2016 Classification of breast lesions using the difference of statistical features. Res. J. Pharm. Biol. Chem. Sci. 7(4): 1365–1372
Bhusri S, Jain S and Virmani J 2016 Breast Lesions Classification using the Amalagation of morphological and texture features. Int. J. Pharma BioSci. 7(2B): 617–624
Rana S, Jain S and Virmani J 2016 SVM-Based characterization of focal kidney lesions from B-Mode ultrasound images. Res. J. Pharm. Biol. Chem. Sci. 7(4): 837
https://www.sas.com/en_in/insights/analytics/data-mining.htnl
https://hackernoon.com/deep-learning-vs-machine-learning-a-simple-explanation-47405b3eef08
Jain S and Chauhan D S 2020 Instance-based learning of marker proteins of carcinoma cells for cell death/survival. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 8(3): 313–332
Dogra J, Jain S and Sood M 2019 Glioma classification of MR brain tumor employing machine learning. Int. J. Innov. Technol. Explor. Eng. 8(8): 2676–2682
Jain S 2020 Computer aided detection system for the classification of non small cell lung lesions using SVM. Curr. Comput. Aided Drug Des. 16(6): 833–840
Li R, Zhang W, Suk H I, Wang L, Li J, Shen D and Ji S 2014 Deep learning based imaging data completion for improved brain disease diagnosis. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention MICCAI-BRATS, pp. 305–312
Jain S and Paul S 2020 Recent Trends in Image and Signal Processing in Computer Vision. Switzerland AG: Springer Nature
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Pal, B., Jain, S. Novel Discrete Component Wavelet Transform for detection of cerebrovascular diseases. Sādhanā 47, 237 (2022). https://doi.org/10.1007/s12046-022-02016-9
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DOI: https://doi.org/10.1007/s12046-022-02016-9