NDLI: Group sparsity based classification for cervigram segmentation

Content Provider	IEEE Xplore Digital Library
Author	Yang Yu Junzhou Huang Shaoting Zhang Restif, C. Xiaolei Huang Metaxas, D.
Copyright Year	2011
Description	Author affiliation: Computer Science & Engineering Department, Lehigh University, Bethlehem, PA, USA (Xiaolei Huang) \|\| CBIM, Rutgers, The State University of New Jersey, Piscataway, USA (Yang Yu; Junzhou Huang; Shaoting Zhang; Restif, C.; Metaxas, D.)
Abstract	This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features are obtained from color or texture information. Using more features makes the classes more separable and increases the segmentation's quality, but also its complexity. However, the properties of these features have not been well investigated. In most cases, a group of features is selected prior to the segmentation process; features with minor contributions to the results are kept and add to the computational cost. We propose feature selection as a significant improvement in this problem. It provides a robust trade-off between segmentation quality and computational complexity. In this work we formulate the cervigram segmentation problem as a feature-selection-based classification method, and we introduce a regularization-based feature-selection algorithm to leverage both the sparsity and clustering properties of the features used. We implemented our method to automatically segment the biomarker AcetoWhite (AW) regions in a dataset of 200 images of the uterine cervix, for which manual segmentation is available. We compare the performance of several regularization-based feature-selection methods. The experimental results demonstrate that on this dataset, our proposed group-sparsity-based method gives overall better results in terms of sensitivity, specificity and sparsity.
Starting Page	1425
Ending Page	1429
File Size	357605
Page Count	5
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872667
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image color analysis Pixel Training Feature extraction Biomedical imaging Shape classification segmentation cervix image biomarker AcetoWhite feature selection group sparsity
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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