Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area
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 ana...
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Samara National Research University
2024-02-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO48-1/480118e.html |
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| author | А.А. Kolchev D.V. Pasynkov I.A. Egoshin I.V. Kliouchkin О.О. Pasynkova |
| author_facet | А.А. Kolchev D.V. Pasynkov I.A. Egoshin I.V. Kliouchkin О.О. Pasynkova |
| author_sort | А.А. Kolchev |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0cb58cd9e07543d5933a47dc1c1298d2 |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-0cb58cd9e07543d5933a47dc1c1298d22025-02-04T11:08:34ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792024-02-0148115716510.18287/2412-6179-CO-1244Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion areaА.А. Kolchev0D.V. Pasynkov1I.A. Egoshin2I.V. Kliouchkin3О.О. Pasynkova4Kazan (Volga region) Federal University, Ministry of Education and Science of Russian FederationKazan (Volga region) Federal University, Ministry of Education and Science of Russian Federation Mari State University, Ministry of Education and Science of Russian Federation; Kazan State Medical Academy - Branch Campus of the Federal State Budgetary Educational Institution of Further Professional Education «Russian Medical Academy of Continuous Professional Education», Ministry of Healthcare of the Russian FederationKazan (Volga region) Federal University, Ministry of Education and Science of Russian Federation; Mari State University, Ministry of Education and Science of Russian Federation Kazan Medical University, Ministry of Health of Russian FederationMari State University, Ministry of Education and Science of Russian FederationThe 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.https://www.computeroptics.ru/eng/KO/Annot/KO48-1/480118e.htmlbreast ultrasoundsolid lesionbenign lesionmalignant lesionclassificationfeature selection |
| spellingShingle | А.А. Kolchev D.V. Pasynkov I.A. Egoshin I.V. Kliouchkin О.О. Pasynkova Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area Компьютерная оптика breast ultrasound solid lesion benign lesion malignant lesion classification feature selection |
| title | Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area |
| title_full | Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area |
| title_fullStr | Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area |
| title_full_unstemmed | Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area |
| title_short | Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area |
| title_sort | classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features the importance of the perifocal lesion area |
| topic | breast ultrasound solid lesion benign lesion malignant lesion classification feature selection |
| url | https://www.computeroptics.ru/eng/KO/Annot/KO48-1/480118e.html |
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