NDLI: Multi-label fast marching and seeded watershed segmentation methods for diagnosis of breast cancer cytology

Content Provider	IEEE Xplore Digital Library
Author	Filipczuk, P. Kowal, M. Obuchowicz, A.
Copyright Year	2013
Description	Author affiliation: Inst. of Control & Comput. Eng., Univ. of Zielona Gora, Gora, Poland (Filipczuk, P.; Kowal, M.; Obuchowicz, A.)
Abstract	Digital cytology plays an increasingly important role in breast cancer diagnosis. However, analysis of cytologic images is a very difficult task. Especially, nuclei segmentation is extremely challenging. In our work on fully automated medical diagnosis system we encountered the problem of densely clustered nuclei. We decided to use a segmentation algorithm that is rather rarely found in the literature. Multi-label fast marching was applied and compared to well-known and extensively used seeded watershed algorithm. In both methods, it is critical to determine the appropriate starting points (seeds). The seeds were determined using a combination of adaptive thresholding in grayscale, clustering in color space and conditional erosion. The proposed segmentation procedure was tested for suitability for diagnosis of the cancer. Experiments were conducted on a set of 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The images were classified as either benign or malignant using 84 features extracted from isolated nuclei. Both methods gave very promising results and showed that our method is effective and can be successfully applied for computer-aided diagnosis system.
Starting Page	7368
Ending Page	7371
File Size	380031
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Feature extraction Breast cancer Biomedical imaging Accuracy Diseases
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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