دورية أكاديمية
[Development of Auto Dense-breast Classification on Mammography Images Using Image Similarity].
| العنوان: | [Development of Auto Dense-breast Classification on Mammography Images Using Image Similarity]. |
|---|---|
| المؤلفون: | Tsuchida T; Department of Radiological Technology, Saiseikai Kawaguchi General Hospital., Negishi T; Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University., Takahashi M; Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences., Mori K; Department of Radiological Technology, Saiseikai Kawaguchi General Hospital.; Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University., Nishimura R; Department of Radiology, Saiseikai Kawaguchi General Hospital. |
| المصدر: | Nihon Hoshasen Gijutsu Gakkai zasshi [Nihon Hoshasen Gijutsu Gakkai Zasshi] 2024 Jun 20; Vol. 80 (6), pp. 616-625. Date of Electronic Publication: 2024 May 22. |
| نوع المنشور: | English Abstract; Journal Article |
| اللغة: | Japanese |
| بيانات الدورية: | Publisher: Nihon Hōshasen Gijutsu Gakkai Country of Publication: Japan NLM ID: 7505722 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1881-4883 (Electronic) Linking ISSN: 03694305 NLM ISO Abbreviation: Nihon Hoshasen Gijutsu Gakkai Zasshi Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Kyoto : Nihon Hōshasen Gijutsu Gakkai |
| مواضيع طبية MeSH: | Mammography*/methods, Humans ; Female ; Breast/diagnostic imaging ; Middle Aged ; Breast Neoplasms/diagnostic imaging ; Aged ; Software ; Image Processing, Computer-Assisted/methods ; Breast Density |
| مستخلص: | Purpose: In Japan, radiologists perform qualitative visual classification to define four categories of mammary gland density. However, an objective estimation of mammary gland density is necessary. To address this, we developed an automatic classification software using image similarity. Methods: We prepared 741 cases of mediolateral oblique images (MLO) for evaluation, and they were diagnosed as normal among the mammography images taken at our hospital. Image matching was performed using the evaluation images and an image database for breast density determination. In this study, the image similarity used zero normalized cross-correlation (ZNCC) as an index. In addition, if the breast thickness is less than 30 mm and each breast density category ZNNC has the same value, the category is evaluated on the fat side. We compared the results of qualitative visual classification and automatic classification methods to assess consistency. Results: The agreement with the subjective breast composition classification was 78.5%, and the weighted kappa coefficient was 0.98. One mismatched case was evaluated on the higher density side with the same ZNCC value between categories and a breast thickness greater than 30 mm. Conclusion: Image similarity provides an excellent estimation of quantification of breast density. This system could contribute to improving the efficiency of the mammography screening system. |
| فهرسة مساهمة: | Keywords: dense breast; image similarity; mammography; template matching |
| تواريخ الأحداث: | Date Created: 20240522 Date Completed: 20240619 Latest Revision: 20240619 |
| رمز التحديث: | 20240620 |
| DOI: | 10.6009/jjrt.2024-1442 |
| PMID: | 38777755 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1881-4883 |
|---|---|
| DOI: | 10.6009/jjrt.2024-1442 |