NDLI: Automatic thresholding using a modified valley emphasis

Content Provider	IET Digital Library
Author	Xing, Jiangwa Yang, Pei Qingge, Letu
Abstract	Otsu's method is one of the most well-known methods for automatic thresholding, which serves as an important algorithm category for image segmentation. However, it fails if the histogram is close to unimodal or has large intra-class variances. To alleviate this limitation, improved Otsu's methods such as the valley emphasis method and weighted object variances method have been proposed, which still yield non-optimal segmentation performance in some cases. In this study, a modified valley metric using second-order derivative is proposed to improve the Otsu's algorithm. Experiments are firstly conducted on five typical test images whose histograms are unimodal, multimodal or have large intra-class variances, and then expanded to a larger data set consisting of 22 cell images. The proposed algorithm is compared with original Otsu's method and existing improved algorithms. Four evaluation metrics including misclassification error, foreground recall, Dice similarity coefficient and Jaccard index are adopted to quantitatively measure the segmentation performance. Results show that the proposed algorithm achieves best segmentation results on both data sets quantitatively and qualitatively. The proposed algorithm adapts the Otsu's method to more image subtypes, indicating a wider application in automatic thresholding and image segmentation field.
Starting Page	536
Ending Page	544
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0176
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	22 Cell Images Automatic Thresholding Biology And Medical Computing Computer Vision And Image Processing Technique Existing Improved Algorithm Image Segmentation Image Segmentation Field Image Subtypes Important Algorithm Category Improved Otsu's Method Intra-class Variances Medical Image Processing Modified Valley Emphasis Modified Valley Metric Using Second-order Derivative Nonoptimal Segmentation Performance Optical, Image And Video Signal Processing Otsu's Algorithm Otsu's Method Patient Diagnostic Method And Instrumentation Typical Test Image Whose Histograms Valley Emphasis Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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