Mario Muštra

886 total citations
36 papers, 534 citations indexed

About

Mario Muštra is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Mario Muštra has authored 36 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Mario Muštra's work include AI in cancer detection (15 papers), Digital Radiography and Breast Imaging (8 papers) and Medical Image Segmentation Techniques (5 papers). Mario Muštra is often cited by papers focused on AI in cancer detection (15 papers), Digital Radiography and Breast Imaging (8 papers) and Medical Image Segmentation Techniques (5 papers). Mario Muštra collaborates with scholars based in Croatia, Bosnia and Herzegovina and United Kingdom. Mario Muštra's co-authors include Mislav Grgić, Krešimir Delač, Rangaraj M. Rangayyan, Jelena Božek, Nikola Kezić, Zdenka Babić, Janja Filipi, Vladimir Risojević, Emil Dumić and Sonja Grgić and has published in prestigious journals such as The Science of The Total Environment, Chemosphere and Remote Sensing.

In The Last Decade

Mario Muštra

34 papers receiving 499 citations

Peers

Mario Muštra
Comparison fields: 5 of 92
  • Artificial Intelligence 284
  • Computer Vision and Pattern Recognition 215
  • Radiology, Nuclear Medicine and Imaging 183
  • Pulmonary and Respiratory Medicine 94
  • Aerospace Engineering 78
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Citations per field, relative to Mario Muštra
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Citations per year, relative to Mario Muštra
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Countries citing papers authored by Mario Muštra

Since Specialization
Citations

This map shows the geographic impact of Mario Muštra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mario Muštra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Muštra more than expected).

Fields of papers citing papers by Mario Muštra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Muštra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mario Muštra. The network helps show where Mario Muštra may publish in the future.

Co-authorship network of co-authors of Mario Muštra

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Muštra. A scholar is included among the top collaborators of Mario Muštra based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mario Muštra. Mario Muštra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 2
2 1
3 10
4 28
5 6
6 2
7 1
8 69
9
Detection of areas containing microcalcifications in digital mammograms
2
10 1
11
Dense tissue segmentation in digitized mammograms
3
12
Enhancement of microcalcifications in digital mammograms
7
13
Application of Gabor filters for detection of dense tissue in mammograms
3
14 7
15
Feature selection for automatic breast density classification
5
16
A survey of mammographic image processing algorithms for bilateral asymmetry detection
5
17
Nipple detection in craniocaudal digital mammograms
4
18
Overview of the DICOM standard
62
19 5
20 1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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