scispace - formally typeset
Search or ask a question
Author

Heng-Da Cheng

Bio: Heng-Da Cheng is an academic researcher from Utah State University. The author has contributed to research in topics: Image segmentation & Fuzzy logic. The author has an hindex of 49, co-authored 234 publications receiving 10214 citations. Previous affiliations of Heng-Da Cheng include Halifax & Harbin Institute of Technology.


Papers
More filters
Journal ArticleDOI
TL;DR: This survey provides a summary of color image segmentation techniques available now based on monochrome segmentation approaches operating in different color spaces and some novel approaches such as fuzzy method and physics-based method are investigated.

1,682 citations

Journal ArticleDOI
TL;DR: Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification, and their advantages and disadvantages are discussed.

628 citations

Journal ArticleDOI
Heng-Da Cheng1, Xiaopeng Cai1, Xiaowei Chen1, Liming Hu1, Xueling Lou1 
TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.

563 citations

Journal ArticleDOI
Heng-Da Cheng1, X. J. Shi1, R. Min1, Liming Hu1, Xiaopeng Cai1, H. N. Du1 
TL;DR: The methods for mass detection and classification for breast cancer diagnosis are discussed, and their advantages and drawbacks are compared.

526 citations

Journal ArticleDOI
TL;DR: The multi-peak generalized histogram equalization (multi-peak GHE) is proposed, which is improved by using multi- peak histogramequalization combined with local information to enhance the images effectively.

306 citations


Cited by
More filters
Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

4,543 citations