تقسیم بندی سلول های سفید خون
ترجمه نشده

تقسیم بندی سلول های سفید خون

عنوان فارسی مقاله: تقسیم بندی سلول های سفید خون از طریق محدودیت های پراکندگی و هندسی
عنوان انگلیسی مقاله: White Blood Cell Segmentation via Sparsity and Geometry Constraints
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی پزشکی
گرایش های تحصیلی مرتبط: بیوالکتریک، پردازش تصاویر پزشکی
کلمات کلیدی فارسی: محدودیت هندسی، محدودیت پراکندگی، تقسیم بندی سلول های سفید خون
کلمات کلیدی انگلیسی: Geometry constraint, sparsity constraint, white blood cell segmentation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2954457
دانشگاه: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350108, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
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: E14043
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Work


III. The Proposed Algorithm


IV. Experimental Results


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy.


Introduction


White blood cells (WBCs) [1], [2] are important defense cells in human blood that consists of five kinds of cells, i.e., neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The WBC segmentation is a challenging task for a variety of medical diagnosis applications. For example, the visual examination of WBCs in blood smears collected under a bright field microscope can be used to diagnose various diseases, such as septic bacterial inflammation, uremia, and various kinds of leukaemia. A number of WBC segmentation methods have been proposed in recent years. In general, existing methods can be divided into two distinct categories: supervised vs. unsupervised WBC segmentation methods. The supervised WBC segmentation methods [3]–[6] formulate the WBC segmentation problem as a multi-class classification problem. They are typically solved in a two-step manner, i.e., first extracting image features and then classifying the extracted features. However, these supervised methods require a large number of annotated training samples, which are often manually labeled and difficult to obtain. Particularly, the manual pixelwise segmentation process is tedious and error-prone for the abundant fine structures in the blood cell imagery. In addition, the training and test images are assumed to be visually similar to minimize the domain shift between training and test images. In practice, this assumption could negatively impact the generalization abilities of these supervised algorithms.

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