aparat aparat telegram1  ar en fa

ساعت کار مرکـز :

شنبه تا 4شنبه: ساعت 8 الی 22 - 5شنبه ها: ساعت 8 الی 14

instagram takipçi satın al indoXploit shell PUBG Lite

ضایعه پستان در ماموگرافی

جدیدترین تکنیک در افتراق ضایعات خوش خیم وبدخیم پستان در ماموگرافی:

Breast mass classification on mammograms using radial local ternary patterns.
Muramatsu C, et al. Comput Biol Med. 2016.
Show full citation

Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based feature, namely, radial local ternary patterns (RLTP), which takes into account the direction of edge patterns with respect to the center of masses for classification of ROIs for benign and malignant masses. Using an artificial neural network (ANN), support vector machine (SVM) and random forest (RF) classifiers, the classification abilities of RLTP were compared with those of the regular local ternary patterns (LTP), rotation invariant uniform (RIU2) LTP, texture features based on the gray level co-occurrence matrix (GLCM), and wavelet features. The performance was evaluated with 376 ROIs including 181 malignant and 195 benign masses. The highest areas under the receiver operating characteristic curves among three classifiers were 0.90, 0.77, 0.78, 0.86, and 0.83 for RLTP, LTP, RIU2-LTP, GLCM, and wavelet features, respectively. The results indicate the usefulness of the proposed texture features for distinguishing between benign and malignant lesions and the superiority of the radial patterns compared with the conventional rotation invariant patterns.

Copyright © 2016 Elsevier Ltd. All rights reserved.
PMID 27015322 [PubMed - as supplied by publisher]
Full text
Full text at journal site
Citation 3 of 6764
Back to results Next
Similar articles

Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.
Mavroforakis ME, et al. Artif Intell Med. 2006.
Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
Chan HP, et al. Med Phys. 2010.
Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound.
Liu H, et al. J Med Imaging (Bellingham). 2014.
Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI.
Nie K, et al. Acad Radiol. 2008.
Usefulness of texture analysis for computerized classification of breast lesions on mammograms.
Pereira RR Jr, et al. J Digit Imaging. 2007.
See all
Full website NIH NLM NCBI Help

خواندن 1233 دفعه
شنبه, 04 ارديبهشت 1395 17:00 چاپ

نظرات (0)

تاکنون نظری برای این مطلب ارسال نشده است.

نظر خود را اضافه کنید.

در قالب میهمان نظر خود را ارسال کنید. ثبت نام کنید یا وارد شوید به حساب کاربریتان.
0 Characters
پیوست ها (0 / 3)
Share Your Location

اطلاعات مرکز

آدرس: اصفهان - خیابان شمس آبادی-چهارراه قصر-مجتمع قصرنور-طبقه 5-واحد501
تلفن‌های تماس: 32240047-031 - 09130748424
تلفن گویا: 9730361
فکس  : 03132240047
کدپستی: 3165844567
ایمیل: info@novintahlilgaran.com


رفتن به بالا