Heng-Da Cheng mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Image processing and Image segmentation. His study in Fuzzy logic, Thresholding, Segmentation, Histogram and Histogram equalization are all subfields of Artificial intelligence. His work carried out in the field of Fuzzy logic brings together such families of science as Pixel and Mammography.
Heng-Da Cheng combines subjects such as Entropy and Early detection with his study of Pattern recognition. His Image processing research incorporates elements of Support vector machine and Pattern recognition. His Region growing and Active contour model study in the realm of Image segmentation interacts with subjects such as Density estimation.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Image segmentation and Fuzzy logic. His Artificial intelligence research incorporates themes from Breast cancer and Breast ultrasound. Heng-Da Cheng has included themes like Computer-aided diagnosis and Early detection in his Breast cancer study.
His Pattern recognition study also includes fields such as
His primary areas of study are Artificial intelligence, Pattern recognition, Breast ultrasound, Image segmentation and Segmentation. As part of his studies on Artificial intelligence, he often connects relevant areas like Computer vision. Heng-Da Cheng has researched Computer vision in several fields, including Computer-aided diagnosis and Robustness.
His research integrates issues of Image quality, Feature, Region of interest, Fuzzy logic and Digital watermarking in his study of Pattern recognition. His study in Image segmentation is interdisciplinary in nature, drawing from both Visualization, Data mining, Watershed and Pattern recognition. Heng-Da Cheng interconnects Field, Benchmark and Conditional random field in the investigation of issues within Segmentation.
Heng-Da Cheng spends much of his time researching Artificial intelligence, Breast ultrasound, Pattern recognition, Image segmentation and Segmentation. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Ultrasound image, Computer vision and Thyroid ultrasound. His research integrates issues of Algorithm, Similarity measure and Key in his study of Computer vision.
His biological study spans a wide range of topics, including Contextual image classification, Image, Benchmark, Supervised learning and Interior point method. His studies deal with areas such as Breast cancer, Invariant and Search algorithm as well as Image segmentation. His Breast cancer research includes elements of Field, Medical imaging, Data science and Conditional random field.
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Color image segmentation: advances and prospects
Heng-Da Cheng;Xihua Jiang;Ying Sun;Jingli Wang.
Pattern Recognition (2001)
Computer-aided detection and classification of microcalcifications in mammograms: a survey
Heng-Da Cheng;Xiaopeng Cai;Xiaowei Chen;Liming Hu.
Pattern Recognition (2003)
Automated breast cancer detection and classification using ultrasound images: A survey
H. D. Cheng;Juan Shan;Wen Ju;Yanhui Guo.
Pattern Recognition (2010)
Approaches for automated detection and classification of masses in mammograms
H. D. Cheng;X. J. Shi;R. Min;L. M. Hu.
Pattern Recognition (2006)
A hierarchical approach to color image segmentation using homogeneity
Heng-Da Cheng;Ying Sun.
IEEE Transactions on Image Processing (2000)
Threshold selection based on fuzzy c-partition entropy approach
Heng-Da Cheng;Jim-Rong Chen;Jiguang Li.
Pattern Recognition (1998)
A novel approach to microcalcification detection using fuzzy logic technique
Heng-Da Cheng;Yui Man Lui;R.I. Freimanis.
IEEE Transactions on Medical Imaging (1998)
A simple and effective histogram equalization approach to image enhancement
Heng-Da Cheng;X. J. Shi.
Digital Signal Processing (2004)
New neutrosophic approach to image segmentation
Yanhui Guo;H. D. Cheng.
Pattern Recognition (2009)
Color image segmentation based on homogram thresholding and region merging
Heng-Da Cheng;Xihua Jiang;Jingli Wang.
Pattern Recognition (2002)
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