In medical imaging, contrast plays a crucial role in determining visual quality and facilitating accurate interpretation, particularly in fields like digital pathology and dermatology where color variations are diagnostically significant. Traditional contrast metrics often focus on intensity variations, potentially overlooking critical color information. This study introduces two novel color contrast metrics designed to quantify color variations at the image level, addressing this gap. The proposed metrics are a generalization of histogram-based methods where perceptual color difference metrics are used to calculate maximum and spread contrast measures. Experimental evaluation, including a study with 28 volunteers assessing image pairs, demonstrated a high correlation (Cohen's kappa of 0.92) between these metrics and human-perceived color contrast. Furthermore, statistical analyses confirmed that these metrics reliably distinguish color differences beyond luminance variations. The metrics proved robust across different parameter settings, as demonstrated by stability tests. Additionally, practical applications were explored in the field of digital pathology. The study concludes that these color contrast metrics align well with human perception and offer a valuable tool for enhancing diagnostic accuracy in medical imaging.
XLSX file - 195K, Listing of gene mutations and aCNA/LOH/cn-LOH in 156 CLL samples analyzed at enrollment and at longitudinal time points.
Supplementary Figures S1-S2 from NF1 Inactivation in Adult Acute Myelogenous Leukemia
Supplementary Figures S1-S5 from A Pathobiological Role of the Insulin Receptor in Chronic Lymphocytic Leukemia
Supplementary Table 4. Catalog of somatic mutations and minimally amplified and deleted regions in the MI-AML cohort.
XLSX file - 18K, Listing of gene mutations and aCNA/LOH/cn-LOH in CLL samples analyzed at enrollment and at longitudinal time points that demonstrated changes. Listing of genes located in small minimally altered regions.
Supplementary Table 2 from Integrated Genomic Profiling of Chronic Lymphocytic Leukemia Identifies Subtypes of Deletion 13q14
PDF File - 84K, Characteristics of 103 CLL patients not analyzed in longitudinal genomic analysis.
Supplementary Table S1 from A Pathobiological Role of the Insulin Receptor in Chronic Lymphocytic Leukemia
Supplementary Data from Aggressive Chronic Lymphocytic Leukemia with Elevated Genomic Complexity Is Associated with Multiple Gene Defects in the Response to DNA Double-Strand Breaks
Related CCR Translation from A Pathobiological Role of the Insulin Receptor in Chronic Lymphocytic Leukemia
sSupplementary Table Legends 1-10, Figure Legend 1 from Integrated Genomic Profiling of Chronic Lymphocytic Leukemia Identifies Subtypes of Deletion 13q14
CCR Translation for This Article from NF1 Inactivation in Adult Acute Myelogenous Leukemia
Supplementary Tables S1-S6 from NF1 Inactivation in Adult Acute Myelogenous Leukemia
Sami Malek合作论文数Division of Hematology/Oncology, Medical School, University of Michigan-Ann Arbor70
Kerby Shedden合作论文数Department of Statistics51
Brian Parkin合作论文数Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan30Lisa Kujawski合作论文数28
Harry Paul Erba合作论文数Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, Duke University School of Medicine;Duke Cancer Institute, Duke University27
Cheng Li (李程)合作论文数School of Life Sciences, Peking University27
Kamlai Saiya-Cork合作论文数22
Mark Kaminski合作论文数University of Michigan21S. Shakhan合作论文数18
Moshe Talpaz合作论文数Division of Hematology Oncology, Department of Internal Medicine, University of Michigan16Diane Roulston合作论文数15Paula Bockenstedt合作论文数13Al-Zoubi Ammar合作论文数13W. Wright合作论文数12A. Dressel合作论文数12
Judith E. Karp合作论文数Department of Oncology and Medicine, School of Medicine, The Johns Hopkins University11A. Purkayastha合作论文数10Collins Roxane合作论文数9
Yang Liu合作论文数Department of Human Development and Quantitative Methodology, College of Education, University of Maryland, College Park9Yildiz Mehmet合作论文数8
Shaomeng Wang合作论文数Wang Lab, University of Michigan;University of Michigan Comprehensive Cancer Center8Yan Liu合作论文数7Yifeng Li合作论文数6Campagnaro Erica合作论文数6
Seymour Erlene合作论文数Division of Hematology/Oncology, University of Michigan Health System6Chris Saddler合作论文数6Kathryn Jacobi合作论文数5yin wang合作论文数5Siân Jones合作论文数5Li Hongxiu合作论文数5
Dale Bixby合作论文数5Kuizon Erlene合作论文数5Jinghui Li合作论文数4
David McClintock合作论文数Department of Pathology, Medical School, University of Michigan-Ann Arbor4
Ulysses Gregory John Balis合作论文数Department of Pathology, Medical School, University of Michigan-Ann Arbor;Center for Global Health Equity, University of Michigan-Ann Arbor;Inspirata, Inc.4
Sandra Camelo-Piragua合作论文数Department of Pathology, Medical School, University of Michigan-Ann Arbor4
Alfred E Chang合作论文数School of Medical, University of Michigan4Lloyd Stoolman合作论文数3Jacob Abel合作论文数University of Tennessee Health Sciences Center3Yin Wang合作论文数3Siri Sahib S Khalsa合作论文数3
Todd Hollon合作论文数MLiNS Lab, Department of Neurosurgery, Medical School, University of Michigan;Michigan Institute for Data Science, University of Michigan3Marentette Lawrence J合作论文数3
Daniel Orringer合作论文数Departments of Neurosurgery,3Jakubowiak Andrzej J合作论文数3Lebovic Daniel合作论文数3
Bert Vogelstein合作论文数Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine3Jianting Long合作论文数3
