تشخیص تومورهای پستان با ماشین بردار پشتیبانی
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تشخیص تومورهای پستان با ماشین بردار پشتیبانی

عنوان فارسی مقاله: تشخیص تومورهای پستان بر اساس استخراج ویژگی لبه با استفاده از ماشین بردار پشتیبانی
عنوان انگلیسی مقاله: Breast tumors recognition based on edge feature extraction using support vector machine
مجله/کنفرانس: پردازش و کنترل سیگنال زیست پزشکی – Biomedical Signal Processing and Control
رشته های تحصیلی مرتبط: پزشکی، مهندسی پزشکی
گرایش های تحصیلی مرتبط: جراحی زنان و زایمان، بیوالکتریک، پردازش تصاویر پزشکی
کلمات کلیدی فارسی: تومور بدخیم، تومور خوش خیم، ویژگی مورفولوژیکی، طبقه بندی تومور
کلمات کلیدی انگلیسی: Malignant tumor، Benign tumor، Morphologic feature، Tumor classification
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.bspc.2019.101825
دانشگاه: College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, 211167, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 3.830 در سال 2019
شاخص H_index: 51 در سال 2020
شاخص SJR: 0.711 در سال 2019
شناسه ISSN: 1746-8094
شاخص Quartile (چارک): Q2 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14202
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


Graphical abstract


۱٫ Introduction


۲٫ Material and methods


۳٫ Results


۴٫ Discussion


۵٫ Conclusions


Declaration of Competing Interest


CRediT authorship contribution statement


Acknowledgments


References

نمونه متن انگلیسی مقاله

Abstract


Nowadays, it is important for the detection of ultrasound images of breast tumors. In this paper, a new ultrasonic image feature extraction algorithm combining edge-based features and morphologic feature information is proposed, which has good effect on benign and malignant identification of breast tumors. This paper mainly studies three features (Sum of maximum curvature, Sum of maximum curvature and peak, Sum of maximum curvature and standard deviation) according to the shape histogram of ultrasound breast tumors from a local perspective. Based on the results of SVM classifier, it was found that the edgebased features have higher classification accuracy. The recognition system would perform better when morphologic features (Roughness, Regularity, Aspect ratio, Ellipticity, Roundness) were incorporated, compared with the control group whose input only with morphologic features. The results show that edge-based features can well describe breast tumors in ultrasound images, and have the potential to be used in breast ultrasound computer-aided design.


Introduction


The statistic report in 2017 shows that the average age of breast cancer patients in China is 48.7 [1]. Breast cancer has become a common disease among women in the current society [2]. Both the breast cancer’s morbidity and mortality are higher than that of other female malignant tumors. Clinical studies have shown that early detection and effective treatments can greatly improve the survival rate of female patients. However, there was no obvious symptoms in the initial when the patient got the cancer, which makes the detection difficult. Therefore, how to discover the lesion area of breast as early as possible so as to improve the cure rate of breast cancer has become a very important topic in the medical field. Ultrasound imaging is a convenient, low-cost, effective, realtime, non-radiation imaging tool, which has been widely used in clinical breast cancer detection [3,4]. In breast tumor diagnosis, the breast ultrasound computer-aided diagnosis (CAD) has been becoming more and more important. It performs better in image preprocessing, segmentation, feature extraction and selection, and tumor classification, including the objective evaluation ∗ Corresponding author. E-mail address: tongying@njit.edu.cn (Y. Tong). results, the classification accuracy, and the diagnostic sensitivity [5,6]. The extraction of different features is crucial in breast ultrasound CAD. Over the past years, research prevailingly concerned the morphologic feature extraction and texture feature extraction [7–۹]. Texture features mainly reflect the surface properties of objects through pixels’ gray distribution and their surrounding spatial neighborhoods’ properties like the clarity, thickness and depth of the image texture.

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