NDLI: Learning-based Method for P53 Immunohistochemically Stained Cell Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Mao, K.Z. Peng Zhao Puay-Hoon Tan
Copyright Year	2005
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ. (Mao, K.Z.; Peng Zhao)
Abstract	In this study, a learning-based color image conversion method is proposed for cell image segmentation. Firstly, we demonstrate that minimum distance-based pixel classification, such as clustering, for color image segmentation in the color space is equivalent to thresholding grayscale images. Motivated by this result, we develop the so called C-G-T procedure for color image segmentation, where color image (C) is first converted into grayscale (G) and thresholding (T) is then performed on the gray image to segment objects out of background. The transform for image conversion is learned from the global pixel distribution in the color space, while the threshold is learned from local pixel distribution of the gray image. The combination of global and local learning makes the C-G-T procedure adaptive and computational efficient. Extensive experiments are performed to verify the effectiveness of our method
Starting Page	3264
Ending Page	3267
File Size	3861835
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1617173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Image segmentation Image converters Pixel Gray-scale Neoplasms Proteins Image color analysis Genetic mutations Immune system
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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