Robert M. Haralick: H-Index 1 · 88K Citations | Citation Map

Unlock Full Map — Pro tier (10K–100K citations)

One-time payment. Full data, CSV export, and embed code.

Viewing Limited Data

Showing up to 10 papers and top 10 citing sources. Upgrade to see all 1 paper and complete citation data.

Robert M. Haralick's Citation Metrics

Bibliometric impact based on 1 indexed publication.

H-Index: 1
i10-Index: 1
Total Citations: 88,142
Citing Countries: 4

As of April 2026.

Robert M. Haralick has an h-index of 1 and 88,142 total citations across 1 publication, with research cited by institutions in 4 countries.

Global Impact Map

Visualizing the geographic distribution of institutions that have cited your work.

Marker

Color

Show home

Top Citing Countries(Limited)

Netherlands(NL)

United States(US)

Poland(PL)

Austria(AT)

Top Citing Institutions(Limited)

Radboud University Medical Center

Memorial Sloan Kettering Cancer Center

Lodz University of Technology

University Medical Center Utrecht

Medical University of Vienna

Eindhoven University of Technology

Visa Evidence Package

Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.

Citer Influence Network

Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 4 of 4 countries (12 total citations).

Scholar

Robert M. Haralick

124

Netherlands

USA

Poland

Austria

React Flow

Letter of Support — Candidate Recommenders

Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.

#1
Peter Gibbs
8.5
Memorial Sloan Kettering Cancer Center
USA· Cited your work 1 time
IndependentTop institutionDiversifies geography
#2
Ida Häggström
8.5
Memorial Sloan Kettering Cancer Center
USA· Cited your work 1 time
IndependentTop institutionDiversifies geography
#3
Babak Ehteshami Bejnordi
5.0
Radboud University Medical Center
Netherlands· Cited your work 1 time
Independent
#4
Bram van Ginneken
5.0
Radboud University Medical Center
Netherlands· Cited your work 1 time
Independent
#5
Andrzej Materka
6.5
Lodz University of Technology
Poland· Cited your work 1 time
IndependentDiversifies geography
#6
Piotr M. Szczypiński
6.5
Lodz University of Technology
Poland· Cited your work 1 time
IndependentDiversifies geography
#7
Georg Langs
6.5
Medical University of Vienna
Austria· Cited your work 1 time
IndependentDiversifies geography
#8
Nico Karssemeijer
5.0
Radboud University Medical Center
Netherlands· Cited your work 1 time
Independent
#9
Geert Litjens
5.0
Radboud University Medical Center
Netherlands· Cited your work 1 time
Independent
#10
Jeroen A. W. M. van der Laak
5.0
Radboud University Medical Center
Netherlands· Cited your work 1 time
Independent

Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.

Cited by 5 papers

5 / 5 independent

Citations flagged as non-independent share the scholar's home institution (University of Kansas). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.

(title pending — citation is still being processed)
· cites “Textural Features for Image Classification”
(title pending — citation is still being processed)
· cites “Textural Features for Image Classification”
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken + 3 more
JAMA (Journal of the American Medical Association)· cites “Textural Features for Image Classification”
(title pending — citation is still being processed)
· cites “Textural Features for Image Classification”
Introduction to Radiomics
Marius E. Mayerhoefer, Andrzej Materka, Georg Langs, Ida Häggström + 3 more
The Journal of Nuclear Medicine· cites “Textural Features for Image Classification”