H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 36 Citations 7,353 141 World Ranking 5455 National Ranking 2671

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the Indian National Academy of Engineering (INAE)

2018 - SPIE Fellow

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Nicholas Petrick focuses on Artificial intelligence, Mammography, Linear discriminant analysis, Pattern recognition and Computer-aided diagnosis. Much of his study explores Artificial intelligence relationship to Research design. He has researched Mammography in several fields, including Contextual image classification, Segmentation, Computer vision and Receiver operating characteristic.

His work carried out in the field of Linear discriminant analysis brings together such families of science as Classifier and Pixel. His study focuses on the intersection of Pattern recognition and fields such as Covariance with connections in the field of Jackknife resampling. Computer-aided diagnosis is a primary field of his research addressed under Radiology.

His most cited work include:

  • The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. (1008 citations)
  • Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images (317 citations)
  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system (282 citations)

What are the main themes of his work throughout his whole career to date?

Nicholas Petrick mostly deals with Artificial intelligence, Pattern recognition, Computer-aided diagnosis, Receiver operating characteristic and Mammography. His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Supine position. His Pattern recognition research is multidisciplinary, incorporating perspectives in Pixel and Data set.

His Computer-aided diagnosis study is concerned with Radiology in general. His studies deal with areas such as Binary classification and Digital mammography as well as Receiver operating characteristic. His studies deal with areas such as Contextual image classification, Image segmentation and Wavelet as well as Linear discriminant analysis.

He most often published in these fields:

  • Artificial intelligence (50.00%)
  • Pattern recognition (30.25%)
  • Computer-aided diagnosis (26.47%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (50.00%)
  • Imaging phantom (13.03%)
  • Nuclear medicine (13.45%)

In recent papers he was focusing on the following fields of study:

Nicholas Petrick mainly investigates Artificial intelligence, Imaging phantom, Nuclear medicine, Pattern recognition and Receiver operating characteristic. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Computer vision. His Nuclear medicine research is multidisciplinary, incorporating elements of Lesion, Scanner, Reconstruction algorithm, Iterative reconstruction and Computed tomography.

His study in the field of Computer-aided diagnosis and Convolutional neural network is also linked to topics like CAD. As a part of the same scientific study, Nicholas Petrick usually deals with the Computer-aided diagnosis, concentrating on Ground truth and frequently concerns with Prostate cancer. His Receiver operating characteristic research includes themes of Nonparametric statistics, Sample size determination, Data mining, Precision and recall and Digital mammography.

Between 2014 and 2021, his most popular works were:

  • Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. (166 citations)
  • Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach (122 citations)
  • 3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT. (54 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary scientific interests are in Artificial intelligence, Medical imaging, Pattern recognition, Computed tomography and Statistics. His Artificial intelligence study combines topics in areas such as Chest ct, Machine learning and Computer vision. The various areas that Nicholas Petrick examines in his Medical imaging study include Research design, Field, Mammography, Clinical study design and Biomarker.

He combines subjects such as Overfitting and Data set with his study of Mammography. His research investigates the connection between Pattern recognition and topics such as Receiver operating characteristic that intersect with issues in Jackknife resampling. His Computed tomography study which covers Pathology that intersects with Cross-validation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

Samuel G. Armato;Geoffrey McLennan;Luc Bidaut;Michael F. McNitt‐Gray.
Medical Physics (2011)

1286 Citations

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

B. Sahiner;Heang-Ping Chan;N. Petrick;Datong Wei.
IEEE Transactions on Medical Imaging (1996)

473 Citations

Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan.
Medical Physics (2002)

397 Citations

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

Heang-Ping Chan;Berkman Sahiner;Mark A. Helvie;Nicholas Petrick.
Radiology (1999)

324 Citations

An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

N. Petrick;Heang-Ping Chan;B. Sahiner;Datong Wei.
IEEE Transactions on Medical Imaging (1996)

300 Citations

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Heang Ping Chan;Berkman Sahiner;Kwok Leung Lam;Nicholas Petrick.
Medical Physics (1998)

298 Citations

Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

Heang-Ping Chan;Datong Wei;Mark A. Helvie;Berkman Sahiner.
Physics in Medicine and Biology (1995)

294 Citations

Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

Berkman Sahiner;Heang Ping Chan;Nicholas Petrick;Mark A. Helvie.
Medical Physics (1998)

288 Citations

Computerized image analysis: estimation of breast density on mammograms.

Chuan Zhou;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie.
Medical Physics (2001)

255 Citations

Improvement of mammographic mass characterization using spiculation measures and morphological features

Berkman Sahiner;Heang-Ping Chan;Nicholas Petrick;Mark A. Helvie.
Medical Physics (2001)

244 Citations

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