Seiichi Murakami

412 total citations
50 papers, 302 citations indexed

About

Seiichi Murakami is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Seiichi Murakami has authored 50 papers receiving a total of 302 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 21 papers in Pulmonary and Respiratory Medicine and 19 papers in Computer Vision and Pattern Recognition. Recurrent topics in Seiichi Murakami's work include Medical Imaging Techniques and Applications (16 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Lung Cancer Diagnosis and Treatment (13 papers). Seiichi Murakami is often cited by papers focused on Medical Imaging Techniques and Applications (16 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Lung Cancer Diagnosis and Treatment (13 papers). Seiichi Murakami collaborates with scholars based in Japan, China and United States. Seiichi Murakami's co-authors include Takatoshi Aoki, Hyoung Seop Kim, Huimin Lu, Michio Ogawa, Joo Kooi Tan, Yoshiko Hayashida, Nobuhiro Oda, Hajime Nakata, Yukunori Korogi and Shinya Shimada and has published in prestigious journals such as Annals of Surgery, Radiology and European Radiology.

In The Last Decade

Seiichi Murakami

45 papers receiving 290 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Seiichi Murakami Japan 10 158 112 78 49 47 50 302
Zhiyong Lin China 9 162 1.0× 153 1.4× 52 0.7× 19 0.4× 31 0.7× 27 277
Anil Pandey India 9 167 1.1× 78 0.7× 50 0.6× 80 1.6× 20 0.4× 38 313
Moozhan Nikpanah United States 12 217 1.4× 85 0.8× 115 1.5× 28 0.6× 33 0.7× 31 350
Benjamin Spilseth United States 11 161 1.0× 231 2.1× 27 0.3× 20 0.4× 75 1.6× 32 391
Ahmad Algohary United States 8 367 2.3× 274 2.4× 84 1.1× 75 1.5× 17 0.4× 16 440
D. Eiss France 11 209 1.3× 314 2.8× 86 1.1× 11 0.2× 60 1.3× 37 466
Minglei Yang China 11 160 1.0× 99 0.9× 46 0.6× 80 1.6× 62 1.3× 35 292
Nadia Caplan Israel 7 121 0.8× 38 0.3× 57 0.7× 54 1.1× 40 0.9× 10 252
Su Yeon Ko South Korea 9 135 0.9× 72 0.6× 32 0.4× 63 1.3× 90 1.9× 21 339
E Linning China 12 306 1.9× 238 2.1× 90 1.2× 38 0.8× 29 0.6× 21 377

Countries citing papers authored by Seiichi Murakami

Since Specialization
Citations

This map shows the geographic impact of Seiichi Murakami'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 Seiichi Murakami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seiichi Murakami more than expected).

Fields of papers citing papers by Seiichi Murakami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seiichi Murakami. 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 Seiichi Murakami. The network helps show where Seiichi Murakami may publish in the future.

Co-authorship network of co-authors of Seiichi Murakami

This figure shows the co-authorship network connecting the top 25 collaborators of Seiichi Murakami. A scholar is included among the top collaborators of Seiichi Murakami 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 Seiichi Murakami. Seiichi Murakami 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
1.
2.
Murakami, Seiichi, et al.. (2023). Evaluation of the clinical utility of temporal subtraction using bone suppression processing in digital chest radiography. Heliyon. 9(1). e13004–e13004. 3 indexed citations
3.
Murakami, Seiichi, et al.. (2018). Detection of Phalange Region Based on U-Net. International Conference on Control, Automation and Systems. 5 indexed citations
4.
Lu, Huimin, et al.. (2018). Enhancement of Bone Metastasis from CT Images Based on Salient Region Feature Registration. International Conference on Control, Automation and Systems. 2 indexed citations
5.
Lu, Huimin, et al.. (2018). Detection of Abnormal Shadows on Temporal Subtraction Images Based on Multi-phase CNN. International Conference on Control, Automation and Systems.
6.
Murakami, Seiichi, et al.. (2018). Registration of Phalange Region from CR Images Based on Genetic Algorithm. International Conference on Control, Automation and Systems.
7.
Murakami, Seiichi, et al.. (2015). Evaluation of the effects of subject thickness on the exposure index in digital radiography. Radiological Physics and Technology. 9(1). 116–120. 8 indexed citations
8.
Kim, Hyoung Seop, et al.. (2012). Classification of lung nodules on temporal subtraction image based on statistical features and improvement of segmentation accuracy. International Conference on Control, Automation and Systems. 1814–1817. 2 indexed citations
9.
Ishikawa, Seiji, et al.. (2012). A Temporal Subtraction Technique for 3-D Non-rigid Warping Method Based on Free Form Deformation from Thoracic MDCT Image. IEICE Technical Report; IEICE Tech. Rep.. 112(271). 11–16. 1 indexed citations
10.
Tsutsumi, Yukitomo & Seiichi Murakami. (2012). Increase in Global Solar Radiation with Total Cloud Amount from 33 Years Observations in Japan. Journal of the Meteorological Society of Japan Ser II. 90(4). 575–581. 9 indexed citations
11.
Fujimoto, Keiji, et al.. (2009). Feasibility of a New Initial Check-up Program for Medical X-ray and Computed Radiography Systems. Japanese Journal of Radiological Technology. 65(10). 1391–1399. 1 indexed citations
12.
Nakamura, Katsumi, Hiroyuki Takahashi, Hiroko Okazaki, et al.. (2000). Clinical Utility of Temporal Subtraction for Detection of Lung Abnormalities on Digital Chest Radiographs. Japanese Journal of Radiological Technology. 56(3). 496–502. 5 indexed citations
13.
Oda, Nobuhiro, Shigehiko Katsuragawa, Kunio Doi, et al.. (1999). Improvement in Visibility of Simulated Lung Nodules on Computed Radiography (CR) Chest Images by Use of Temporal Subtraction Technique. Japanese Journal of Radiological Technology. 55(11). 1101–1108. 1 indexed citations
14.
Matsushita, Sho, Seiichi Murakami, Hiroshi Fujita, et al.. (1997). Augmentation of immune response by an analog of the antigenic peptide in a human T-cell clone recognizing mutated Ras-derived peptides. Human Immunology. 52(1). 22–32. 11 indexed citations
15.
Oda, Nobuhiro, et al.. (1996). Optimal Beam Quality for Chest Computed Radiography. Investigative Radiology. 31(3). 126–131. 28 indexed citations
16.
Murakami, Seiichi, et al.. (1995). DATA COMPRESSION FOR CHEST RADIOGRAPHY AND MAMMOGRAPHY USING COMPUTED RADIOGRAPHY. Japanese Journal of Radiological Technology. 51(1). 13–18. 3 indexed citations
17.
Funama, Yoshinori, et al.. (1995). MEASUREMENT OF THE AVERAGE VALUES OVER OBSERVER'S CONFIDENCE-RATING LEVELS BY THE METHOD OF SUCCESSIVE CATEGORIES. Japanese Journal of Radiological Technology. 51(9). 1197–1202. 1 indexed citations
18.
Oda, Nobuhiro, et al.. (1994). OPTIMAL BEAM QUALITY FOR ABDOMINAL COMPUTED RADIOGRAPHY. Japanese Journal of Radiological Technology. 50(5). 622–629. 1 indexed citations
19.
Takebe, Kazuo, et al.. (1992). CLinical Study On Cefclidin. Chemotherapy. 40. 687–690. 1 indexed citations
20.
Morishita, Junji, et al.. (1991). LOW-SCATTER RADIOGRAPHY USING COMPUTED RADIOGRAPHY : A NEW CONCEPT IN IMPROVING IMAGE QUALITY. Japanese Journal of Radiological Technology. 47(4). 610–619. 3 indexed citations

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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