Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area

Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area

Автор: Kolchev A.A., Pasynkov D.V., Egoshin I.A., Kliouchkin I.V., Pasynkova O.O.

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 1 т.48, 2024 года.

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The amount of ultrasound (US) breast exams continues to grow because of the wider endorsement of breast cancer screening programs. When a solid lesion is found during the US the primary task is to decide if it requires a biopsy. Therefore, our goal was to develop a noninvasive US grayscale image analysis for benign and malignant solid breast lesion differentiation. We used a dataset consisting of 105 ultrasound images with 50 benign and 55 malignant non-cystic lesions. Features were extracted from the source image, the image of the gradient module after applying the Sobel filter, and the image after the Laplace filter. Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. The LASSO method was employed to determine the most significant features for classification. Finally, the lesion classification was carried out by various methods. The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. Out of the 13 features selected by the LASSO method, four described the perilesional tissue, two represented the inner area of the lesion and five described the image of the gradient module. The final model achieved a sensitivity of 98%, specificity of 96%, and accuracy of 97%. Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. Features with a low AUC index (less than 0.6 in our case) can also be important for improving the quality of classification.

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Breast ultrasound, solid lesion, benign lesion, malignant lesion, classification, feature selection

Короткий адрес: https://sciup.org/140303292

IDR: 140303292   |   DOI: 10.18287/2412-6179-CO-1244

Список литературы Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area

  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 2018; 68: 394-424. DOI: 10.3322/caac.21492.
  • Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, Straif K. Breast-cancer screening – viewpoint of the IARC Working Group. N Engl J Med 2015; 372: 2353-2358. DOI: 10.1056/NEJMsr1504363.
  • Raza S, Goldkamp AL, Chikarmane SA, Birdwell RL. US of breast masses categorized as BI-RADS 3, 4, and 5: Pictorial review of factors influencing clinical management 1. Radiographics 2010; 30(5): 1199-1213. DOI: 10.1148/rg.305095144.
  • Guo R, Lu G, Qin B, Fei B. Ultrasound imaging technologies for breast cancer detection and management: A review. Ultrasound Med Biol 2018; 44(1): 37-70. DOI: 10.1016/j.ultrasmedbio.2017.09.012.
  • Krizmanich-Conniff KM, Paramagul C, Patterson SK, Helvie MA, Roubidoux MA, Myles JD, Jiang K, Sabel M. Triple receptor–negative breast cancer: Imaging and clinical characteristics. AJR Am J Roentgenol 2012; 199(2): 458-464. DOI: 10.2214/AJR.10.6096.
  • Klimonda Z, Karwat P, Dobruch-Sobczak K, Piotrzkowska-Wroblewska H, Litniewski J. Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue. Sci Rep 2019; 9: 7963. DOI: 10.1038/s41598-019-44376-z.
  • Bahareh B, Hamze R, Tehrani Ali KZ, Hassan R. Deep classification of breast cancer in ultrasound images: more classes, better results with multi-task learning. Proc SPIE 2021; 11602: 116020S. DOI: 10.1117/12.2581930.
  • Nemat H, Fehri H, Ahmadinejad N, Frangi AF, Gooya A. Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Med Phys 2018; 45(9): 4112-4124. DOI: 10.1002/mp.13082.
  • Telagarapu P, Poonguzhali S. Analysis of contourlet texture feature extraction to classify the benign and malignant tumors from breast ultrasound images. Int J Eng Technol 2014; 6(1): 293-305.
  • Badawy SM, Mohamed AE-NA, Hefnawy AA, Zidan HE, GadAllah MT, El-Banby GM. Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning–A feasibility study. PLoS ONE 2021; 16: e0251899. DOI: 10.1371/journal.pone.0251899.
  • Jabeen K., Khan MA, Alhaisoni M, Tariq U, Zhang Y-D, Hamza A, Mickus A, Damasevicius R. Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors 2022; 22(3): 807. DOI: 10.3390/s22030807.
  • Gonzalez-Luna FA, Hernandez-Lopez J, Gomez-Flores W. A performance evaluation of machine learning techniques for breast ultrasound classification. 2019 16th Int Conf on Electrical Engineering, Computing Science and Automatic Control (CCE) 2019; 1-5. DOI: 10.1109/ICEEE.2019.8884547.
  • Hsu SM, Kuo WH, Kuo FC, Liao YY. Breast tumor classification using different features of quantitative ultrasound parametric images. Int J CARS 2019; 14: 623-633. DOI: 10.1007/s11548-018-01908-8.
  • Wei M, Wu X, Zhu J, Liu P, Luo Y, Zheng L, Du Y. Multi-feature fusion for ultrasound breast image classification of benign and malignant. IEEE 4th Int Conf on Image, Vision and Computing (ICIVC) 2019; 474-478. DOI: 10.1109/ICIVC47709.2019.8980898.
  • Prabhakar T, Poonguzhali S. Assessment of texture feature extraction to classify the benign and malignant lesions from breast ultrasound images. In Book: Dash S, Naidu P, Bayindir R, Das S, eds. Artificial intelligence and evolutionary computations in engineering systems. Proceedings of ICAIECES 2017. Singapore: Springer Nature Singapore Pte Ltd; 2018: 725-732. DOI: 10.1007/978-981-10-7868-2_69.
  • Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, Lv G, Zhuang J. A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput Math Methods Med 2020; 2020: 5894010. DOI: 10.1155/2020/5894010.
  • Moustafa AF, Cary TW, Sultan LR, Schultz SM, Conant EF, Venkatesh SS, Sehgal CM. Color Doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics 2020; 10(9): 631. DOI: 10.3390/diagnostics10090631.
  • Mishra AK, Roy P, Bandyopadhyay S, Das SK. Breast ultrasound tumour classification: A machine learning–radiomics based approach. Expert Syst 2021; 38: e12713. DOI: 10.1111/exsy.12713.
  • Faust O, Acharya UR, Meiburger KM, Molinari F, Koh JEW, Yeong CH, Kongmebhol P, Ng KH. Comparative assessment of texture features for the identification of cancer in ultrasound images: A review. Biocybern Biomed Eng 2021; 38(2): 275-296. DOI: 10.1016/j.bbe.2018.01.001.
  • Shia WC, Chen DR. Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine. Comput Med Imaging Graph 2021; 87: 101829. DOI: 10.1016/j.compmedimag.2020.101829.
  • Rodriguez-Cristerna A, Gomez-Flores W, de Albuquerque Pereira WC. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes. Comput Methods Programs Biomed 2018; 153: 33-40. DOI: 10.1016/j.cmpb.2017.10.004.
  • Wu T, Sultan LR, Tian J, Cary TW, Sehgal CM. Machine learning for diagnostic ultrasound of triple-negative breast cancer. Breast Cancer Res Treat 2019; 173(2): 365-373. DOI: 10.1007/s10549-018-4984-7.
  • Xiong H, Sultan LR, Cary TW, Schultz SM, Bouzghar G, Sehgal CM. The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images. Ultrasound 2017; 25(2): 98-106. DOI: 10.1177/1742271X17690425.
  • Kolchev AA, Pasynkov DV, Egoshin IA, Kliouchkin IV, Pasynkova OO. Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images. Int J CARS 2022; 17: 219-228. DOI: 10.1007/s11548-021-02522-x.
  • Yang Y, Li W, Kang Y, Guo Y, Yang K, Li Q, Liu Y, Yang C, Chen R, Chen H, Li X, Cheng L. A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy. Math Biosci Eng 2022; 19(4): 4145-4165. DOI: 10.3934/mbe.2022191.
  • Chowdhury A, Razzaque RR, Muhtadi S, Shafiullah A, Ul Islam Abir E, Garra BS, Kaisar Alam S. Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework. Ultrasonics 2022; 124: 106744. DOI: 10.1016/j.ultras.2022.106744.
  • Liu L, Li K, Qin W, Wen T, Li L, Wu J, Gu J. Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images. Med Biol Eng Comput 2018; 56(2): 183-199. DOI: 10.1007/s11517-017-1770-3.
  • Wei M, Du Y, Wu X, Zhu J. Automatic classification of benign and malignant breast tumors in ultrasound image with texture and morphological features. 2019 IEEE 13th Int Conf on Anti-counterfeiting, Security, and Identification (ASID) 2019; 126-130. DOI: 10.1109/ICASID.2019.8925194.
  • Hsieh YH, Hsu FR, Dai ST, Huang HY, Chen DR, Shia WC. Incorporating the breast imaging reporting and data system lexicon with a fully convolutional network for malignancy detection on breast ultrasound. Diagnostics 2021; 12(1): 66. DOI: 10.3390/diagnostics12010066.
  • Zhou Z, Wu S, Chang KJ, Chen WR, Chen YS, Kuo WH, Lin CC, Tsui PH. Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features. J Med Biol Eng 2015; 35(2): 178-187. DOI: 10.1007/s40846-015-0031-x.
  • Agafonova YuD, Gaidel AV, Zelter PM, Kapishnikov AV. Efficiency of machine learning algorithms and convolutional neural network for detection of pathological changes in MR images of the brain. Computer Optics 2020; 44(2): 266-273. DOI: 10.18287/2412-6179-CO-671.
  • Vinokurov VO, Matveeva IA, Khristoforova YA, Myakinin OO, Bratchenko IA, Bratchenko LA, Moryatov AA, Kozlov SG, Machikhin AS, Abdulhalim I, Zakharov VP. Neural network classifier of hyperspectral images of skin pathologies. Computer Optics 2021; 45(6): 879-886. DOI: 10.18287/2412-6179-CO-832.
  • Zhu C. Machine reading comprehension: Algorithms and practice. Oxford: Elsevier; 2021. ISBN:‎ 978-0-323-90118-5.
  • Kriti, Virmani J, Agarwal R. Deep feature extraction and classification of breast ultrasound images. Multimed Tools Appl 2020; 79(38): 27257-27292. DOI: 10.1007/s11042-020-09337-z.
  • Bochkovskiy A, Wang CY, Liao HY. YOLOv4: Optimal speed and accuracy of object detection. arXiv Preprint. 2020. Source: https://arxiv.org/abs/2004.10934.
  • Zeimarani B, Costa MGF, Nurani NZ, Bianco SR, De Albuquerque Pereira WC, Filho CFFC. Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access 2020; 8: 133349-133359. DOI: 10.1109/ACCESS.2020.3010863.
  • Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK. Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics 2021; 11(10): 1859. DOI: 10.3390/diagnostics11101859.
  • Cao Z, Yang G, Li X, Chen Q, Wu J. Multitask classification method based on label correction for breast tumor ultrasound images. Neural Process Lett 2021; 53: 1453-1468. DOI: 10.1007/s11063-021-10455-4.
  • Cao Z, Duan L, Yang G, Yue T, Chen Q. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med Imaging 2019; 19(1): 51. DOI: 10.1186/s12880-019-0349-x.
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