Fusion Framework for Morphological and Multispectral Textural Features for Identification of Endometrial Tuberculosis
Pages 187 - 195
Abstract
Endometrial Tuberculosis (ETB) is primarily diagnosed in infertile females as a fallout of Female Genital Tuberculosis (FGTB). An effective and fast computational method to diagnose ETB from Transvaginal ultrasound (TVUS) images is of great importance to the community. The objective of this paper is to obtain an optimal subset of features for an effective and discriminative analysis of TVUS images for identifying ETB. The TVUS images from different medical centers in India have been collected under expert supervision from female patients. Texture and Morphological features effectively capture the observations made by the experts for identifying the problem in hand. Therefore a fusion framework model is proposed where the extracted image features are fused and an optimal subset of features is obtained for identification. Multiresolution transformation of ill-defined TVUS images highlights the directional, multi- scale spectral textural features. Therefore, to obtain discriminatory textural features, images are transformed using Non-Subsampled Contourlet Transformation (NSCT) before feature extraction. Experimental results of the fusion model for classification show significant improvements and prove to be more efficient. The proposed methodology records an F-score of 0.845 with a sensitivity score of 0.818 for the dataset available. A feature reduction of 64.5% is attained for the classification of the dataset after feature selection.
References
[1]
World Health Organization: “Global tuberculosis report 2019”, www.who.int/tb/publications/global_report
[2]
Sharma, J.B., Dharmendra, S., Agarwal, S., 2016, “Genital tuberculosis and infertility” Fertility Science and Research,3: 6-18
[3]
Djuwantono, T., Permadi, W., Septiani, L., 2017, “Female genital tuberculosis and infertility: serial cases report in Bandung, Indonesia and literature review”, BMC research notes, 10(1), 1683.
[4]
Naik N,S, Chandanwale A., Kadam D., Sambarey P.W., DhumalG., 2021, Detection of genital tuberculosis among women with infertility using best clinical practices in India: An implementation study, Indian Journal of Tuberculosis,68(1), 85-91.
[5]
T. Steifer and M. Lewandowski, “Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification,” Biomedical Signal Processing and Control, vol. 51, pp. 235–242, 2019.
[6]
Latha, S., Samiappan, D., & Kumar, R., 2020, “Carotid artery ultrasound image analysis: A review of the literature”, Proceedings of the institution of mechanical engineers, Part H: journal of engineering in medicine, 234(5), 417-443.
[7]
Su, Y., Wang, Y., Jiao, J., & Guo, Y., 2011,“Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features”, the open medical informatics journal, 5(1).
[8]
Daoud, M. I., Bdair, T. M., Al-Najar, M., & Alazrai, R., 2016, “A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses”, Computational and mathematical methods in medicine, 2016, 6740956. https://doi.org/10.1155/2016/6740956
[9]
Daoud MI, Abdel-Rahman S, Bdair TM, Al-Najar MS, Al-Hawari FH, Alazrai R., 2020, “Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features”, Sensors (Basel).; 20(23):6838. 33265900; PMCID: PMC7730057.
[10]
Hsu SM, Kuo WH, Kuo FC, Liao YY, 2019, “Breast tumor classification using different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg.”, 14(4):623-633.
[11]
Yang, X., Qiu, S., & Luo, Q., 2020, Feature-Based Discrimination of Thyroid Cancer on Ultrasound Images. In 2020 IEEE 3rd International Conference on Electronics Technology, pp. 834-839.
[12]
Haralick R. M., Shanmugam K., Dinstein I.1973, “Textural Features for Image Classification”. IEEE Transactions on Systems, Man, and Cybernetics, 3: 610–621.
[13]
Kumar, D., 2020, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA”, Procedia Computer Science, 167: 1722-1731.
[14]
Brehar, R., Mitrea, D., Nedevschi, S., 2019, “Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning”, IEEE 15th International Conference on Intelligent Computer Communication and Processing, 491-497.
[15]
Dandan, L., Huanhuan,M.,et al, 2019, “Classification of diffuse liver diseases based on ultrasound images with multimodal features”, IEEE International Instrumentation and Measurement Technology Conference, 1-5.
[16]
Dhaygude, P. S., Handore, S. M., 2016, “Feature Extraction of Thyroid Nodule US Images Using GLCM", International Journal of Science and Research, 51.
[17]
Do, M. N., and Vetterli, M., 2002, “Contourlets: a directional multiresolution image representation”, Proceedings. International Conference on Image Processing, 1, pp. I-I.
[18]
Wei, M., Du, Y., Wu, X., Su, Q., Zhu, J., Zheng, L., & Zhuang, J., 2020, “A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images”, Computational and Mathematical Methods in Medicine.
[19]
A. A. Ardakani, A. Gharbali, and A. Mohammadi, 2015, “Classification of breast tumors using sonographic texture analysis,” Journal of Ultrasound in Medicine, vol. 34, no. 2, pp. 225–231.
[20]
R. Biswas, A. Nath, and S. Roy, 2016, “Mammogram classification using gray-level co-occurrence matrix for diagnosis of breast cancer,” International Conference on Micro- Electronics and Telecommunication Engineering (ICMETE), pp. 161–166.
[21]
M. Abdel-Nasser, J. Melendez, A. Moreno, O. A. Omer, and D. Puig,2017, “Breast tumour classification in ultrasound images using texture analysis and super-resolution methods,” Engineering Applications of Artificial Intelligence, vol. 59, pp. 84–92.
[22]
W. Gómez Flores, W. C., A. Pereira, and A. F. C. Infantosi, 2015, “Improving classification performance of breast lesions on ultrasonography,” Pattern Recognition, vol. 48, no. 4, pp. 1125–1136.
[23]
R. V. Menon, P. Raha, S. Kothari, S. Chakraborty, I. Chakrabarti, and R. Karim, 2015, “Automated detection and classification of mass from breast ultrasound images,” 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4.
[24]
A. Rodríguez-Cristerna, W. Gómez-Flores, and W. C. de Albuquerque Pereira, 2018 “A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes,” Computer Methods and Programs in Biomedicine, vol. 153, pp. 33–40.
[25]
F. A. González-Luna, J. Hernández-López, and W. Gomez- Flores, 2019, “A performance evaluation of machine learning techniques for breast ultrasound classification,”,16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–5.
[26]
Garg, V., Sahoo, A., &Saxena, V., 2021, “Enhanced textural analysis for endometrial tuberculosis identification from ultrasound images”, International Journal of Information Technology, 1-10.
[27]
Telagarapu, P., & Poonguzhali, S., 2014, “Analysis of contourlet texture feature extraction to classify the benign and malignant tumours from breast ultrasound images. International Journal of Engineering and Technology, 6(1), 239-305.
[28]
Wei, M., Wu, X., Zhu, J., 2019, “Multi-feature Fusion for Ultrasound Breast Image Classification of Benign and Malignant” IEEE 4th International Conference on Image, Vision and Computing, 474-478.
[29]
Da Cunha, A.L., Zhou, J., & Do, M. N., 2006, “The non-subsampled contourlet transform: theory, design, and applications”, IEEE transactions on image processing,15(10), 3089-3101.
[30]
Chen, Yi-Wei, and Chih-Jen Lin., 2006 "Combining SVMs with various feature selection strategies," Feature extraction. Springer, Berlin, Heidelberg, 315-324.
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August 2021
483 pages
ISBN:9781450389204
DOI:10.1145/3474124
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Published: 04 November 2021
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IC3 '21
IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing
August 5 - 7, 2021
Noida, India
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