Abstract
Segmentation and classification of ultrasonic breast images is extremely critical for medical diagnosis. Over the last years, various techniques have already been presented for this objective. In this paper, a proposed framework is presented to segment a given ultrasonic image with breast tumor and classify the tumor as being benign or malignant. The proposed framework depends on an active contour segmentation model to determine the tumor region, and then extract it from the ultrasonic image. After that, the Discrete Wavelet Transform (DWT) is used to extract features from the segmented images. Then, the dimensions of the resulting features are reduced by applying feature reduction approaches, namely, the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) and both of them together. The obtained features are submitted to a statistical classifier and the strategy of voting is used to classify the tumor. In the simulation work, 160 benign and malignant breast tumor images collected from Sirindhorn International Institute of Technology (SIIT) website are used. The average processing time for a 256 × 256 image on a laptop with Core i5, 2.3 GHz processor and 8GB RAM is 1.8 s. From the simulation results, it is found that the utilization of the PCA approach provides the best accuracy of 99.23% among the three feature reduction approaches applied. Finally, the proposed framework is compared with the Support Vector Machine (SVM) classification to evaluate its performance in terms of accuracy, sensitivity, precision, and specificity. It is noticed that the proposed framework is efficient and rapid, and it can be applied for ultrasonic breast image segmentation and classification, and thus it can assist the specialists to segment and decide whether a tumor is benign or malignant.
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References
Barman PC (2011) MRI image segmentation using level set method and implement a medical diagnosis system. Computer Science & Engineering: An International Journal (CSEIJ), vol 1(5)
Camacho J, Pico J, Ferrer A (2010) Corrigendum to the best approaches in the on-line monitoring of batch processes based on PCA: does the modelling structure matter? Anal Chim Acta 658(1)
Chen W, Liu T, Wang B (2011) Ultrasonic image classification based on support vector machine with two independent component features. Computers & Mathematics with Applications 62(7):2696–2703
Comparative study of dimensionality reduction techniques using PCA and LDA for content based image retrieval (2015). Conference: International Conference on Computer Science and Information Technology.
Ding J, Cheng HD, Huang J, Liu J, Zhang Y (2012) Breast ultrasound image classification technique based on multiple-instance learning. US National Library of Medicine National Institutes of Health 25(5)
Emin Tagluk M, Akin M, Sezgin N (2010) Classification of sleep apnea by using wavelet transform and artificial neural networks. Expert Syst Appl 37(2):1600–1607
Gupta D, Choubey S (2015) Discrete wavelet transform for image processing. International Journal of Emerging Technology and Advanced Engineering (IJETAE), vol. 4(3)
Hagar AAM, Alshewimy MAM, Faheem Saidahmed MT (2016) A new object recognition framework based on PCA, LDA, and K-NN. In: 2016 11th International Conference on Computer Engineering And Systems (ICCES). IEEE, pp. 141–146 Google Scholar
Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. 2010 International Conference on Information and Communication Technology Convergence (ICTC)
Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: Proceedings of the 2011 international conference on computational science and its applications, vol 5, pp 458–458
Leng L, Zhang S, Bi X, Khan MK (2012) Two-dimensional cancelable biometric scheme. 2012 International Conference on Wavelet Analysis and Pattern Recognition, pp 164–169
Li H (2016) Accurate and efficient classification based on common principal components analysis for multivariate time series. Neurocomputing 171:744–753
Lu L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and Palmprint recognition in DCT domain. International Journal of Physical Sciences 5(17):467–471
Makandar A, Halalli B (2018) Mammography image analysis using wavelet and statistical features with SVM classifier. In: Proceedings of international conference on cognition and recognition (ICCR). Springer, Singapore, pp 371–382
Malik B, Klock J, Wiskin J, Lenox M (2016) Objective breast tissue image classification using quantitative transmission ultrasound tomography. Nature Research Journals, Scientific Reports vol 6(38857)
Murgante B, Gervasi O, Iglesias A, Taniar D, Apduhan BO (2011) Computational science and its applications. In: Lecture notes in computer science, vol 6786. Springer, Berlin, ICCSA, p 2011
Ng SC (2017) Principal component analysis to reduce dimension on digital image. Procedia Computer Science 111:113–119
Nosrati SM (2015) Prior knowledge for targeted object segmentation in medical images. In: Phd thesis
Prabhakar T, Poonguzhali S (2017) Analysis of level set methods for lesion segmentation on breast ultrasound images. International Journal of Pure and Applied Mathematics 114(10):119–132
Sadek I, Elawady M, Stefanovski V (2016) Automated breast lesion segmentation in ultrasound images. researchgate.net/publication/308692196
Shereena VB, David JM (2015) Significance of dimensionality reduction in image processing. Signal & Image Processing: An International Journal (SIPIJ) 6(3)
Tammireddy PR (2014) Image reconstruction using wavelet transform with extended fractional Fourier transform. Msc. Thesis
Vikhe PS, Thool VR (2016) Mass detection in mammographic images using wavelet processing and adaptive threshold technique. Journal of Medical Systems archive 40(4):1–16
Xiang B (2013) Knowledge-based image segmentation using sparse shape priors and high-order MRFs. In: Phd thesis
Xiao J, Yi B, Xu L, Xie H (2008) An image segmentation algorithm based on level set using discontinue PDE. First International Conference On Intelligent Networks And Intelligent Systems. ICINIS'08:503–506
Yang Z, Bogovic JA, Carass A, Ye M, Searson PC, Prince JL (2013) Automatic cell segmentation in fluorescence images of confluent cell monolayers using multi-object geometric deformable model. Image Processing, Medical Imaging, Proc. of SPIE
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Mahmoud, A.A., El-Shafai, W., Taha, T.E. et al. A statistical framework for breast tumor classification from ultrasonic images. Multimed Tools Appl 80, 5977–5996 (2021). https://doi.org/10.1007/s11042-020-08693-0
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DOI: https://doi.org/10.1007/s11042-020-08693-0