What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
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 most cited work include:
- Color image segmentation: advances and prospects (1339 citations)
- Computer-aided detection and classification of microcalcifications in mammograms: a survey (469 citations)
- Automated breast cancer detection and classification using ultrasound images: A survey (447 citations)
What are the main themes of his work throughout his whole career to date?
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
- Entropy together with Neutrosophic set,
- Thresholding that connect with fields like Balanced histogram thresholding.
In the field of Image segmentation, his study on Scale-space segmentation, Region growing and Active contour model overlaps with subjects such as Social connectedness.
His Image processing study combines topics in areas such as Algorithm and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (84.08%)
- Pattern recognition (47.35%)
- Computer vision (45.71%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (84.08%)
- Pattern recognition (47.35%)
- Breast ultrasound (24.08%)
In recent papers he was focusing on the following fields of study:
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.
Between 2012 and 2021, his most popular works were:
- Automatic Breast Ultrasound Image Segmentation: A Survey (73 citations)
- Automatic Breast Ultrasound Image Segmentation: A Survey (73 citations)
- Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains (61 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
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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