Jachih Fu

428 total citations
25 papers, 322 citations indexed

About

Jachih Fu is a scholar working on Surgery, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jachih Fu has authored 25 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Surgery, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jachih Fu's work include Advanced MRI Techniques and Applications (5 papers), Image and Signal Denoising Methods (4 papers) and Gastroesophageal reflux and treatments (3 papers). Jachih Fu is often cited by papers focused on Advanced MRI Techniques and Applications (5 papers), Image and Signal Denoising Methods (4 papers) and Gastroesophageal reflux and treatments (3 papers). Jachih Fu collaborates with scholars based in Taiwan, United States and China. Jachih Fu's co-authors include Stephen T.C. Wong, Jyh‐Wen Chai, Jinn‐Yi Yeh, Han‐Chung Lien, K Mori, Nagayoshi Kasashima, Cheng-Jung Wu, J.-Y. Yeh, Yang‐Kun Ou and Xinhua Cao and has published in prestigious journals such as IEEE Transactions on Power Electronics, International Journal of Production Research and International Journal of Machine Tools and Manufacture.

In The Last Decade

Jachih Fu

23 papers receiving 302 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jachih Fu Taiwan 10 154 103 96 46 40 25 322
Enric Martı́ Spain 11 228 1.5× 53 0.5× 55 0.6× 24 0.5× 34 0.8× 25 405
Edgard Nyssen Belgium 9 187 1.2× 66 0.6× 80 0.8× 36 0.8× 52 1.3× 41 281
Tati L. R. Mengko Indonesia 12 147 1.0× 67 0.7× 103 1.1× 48 1.0× 70 1.8× 64 386
Hanxue Gu United States 5 140 0.9× 100 1.0× 143 1.5× 22 0.5× 64 1.6× 12 378
Haoyu Dong United States 4 139 0.9× 100 1.0× 128 1.3× 19 0.4× 51 1.3× 10 361
Haidar Almubarak Saudi Arabia 11 134 0.9× 254 2.5× 141 1.5× 33 0.7× 70 1.8× 16 490
Sundaresh Ram United States 11 254 1.6× 58 0.6× 61 0.6× 56 1.2× 42 1.1× 46 431
Irena Galić Croatia 12 197 1.3× 108 1.0× 139 1.4× 19 0.4× 64 1.6× 53 431
Benoît Naegel France 13 244 1.6× 39 0.4× 74 0.8× 59 1.3× 45 1.1× 35 381
Dongmei Zhu China 6 82 0.5× 76 0.7× 98 1.0× 25 0.5× 42 1.1× 11 272

Countries citing papers authored by Jachih Fu

Since Specialization
Citations

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

Fields of papers citing papers by Jachih Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jachih Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Jachih Fu. A scholar is included among the top collaborators of Jachih Fu 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 Jachih Fu. Jachih Fu 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.
Fu, Jachih, Chen‐Chi Wang, Chun‐Yi Chuang, et al.. (2023). A cascade deep learning model for diagnosing pharyngeal acid reflux episodes using hypopharyngeal multichannel intraluminal Impedance-pH signals. Intelligence-Based Medicine. 8. 100131–100131. 2 indexed citations
3.
Chen, Yen‐Yang, Chen‐Chi Wang, John Y. Kao, et al.. (2023). Validation of Pharyngeal Acid Reflux Episodes Using Hypopharyngeal Multichannel Intraluminal Impedance-pH. Journal of Neurogastroenterology and Motility. 29(1). 49–57. 7 indexed citations
4.
Wang, Chen‐Chi, Chun‐Yi Chuang, Yung‐An Tsou, et al.. (2023). Distal Mean Nocturnal Baseline Impedance Predicts Pathological Reflux of Isolated Laryngopharyngeal Reflux Symptoms. Journal of Neurogastroenterology and Motility. 29(2). 174–182. 5 indexed citations
5.
Ou, Yang‐Kun, et al.. (2014). Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging. Magnetic Resonance in Medical Sciences. 13(3). 155–166. 9 indexed citations
6.
Fu, Jachih, et al.. (2013). Computer-aided diagnosis for knee meniscus tears in magnetic resonance imaging. Journal of Industrial and Production Engineering. 30(2). 67–77. 11 indexed citations
7.
Chai, Jyh‐Wen, Hsian‐Min Chen, Chih‐Ming Chiang, et al.. (2011). Correction of left ventricular wall thickening from short‐axis cine MRI for basal‐descent through‐plane motion. Journal of Magnetic Resonance Imaging. 33(2). 464–473. 6 indexed citations
8.
Fu, Jachih, et al.. (2009). Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Computerized Medical Imaging and Graphics. 34(4). 308–320. 42 indexed citations
9.
Chih, Wen‐Hai, Jachih Fu, & Kung‐Jeng Wang. (2007). A NEURAL NETWORK BASED APPROACH FOR TOLERANCE ANALYSIS. Journal of the Chinese Institute of Industrial Engineers. 24(5). 366–377. 1 indexed citations
10.
Fu, Jachih, et al.. (2005). Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics. 29(6). 419–429. 96 indexed citations
11.
Yeh, J.-Y., et al.. (2005). Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images. Computer Methods and Programs in Biomedicine. 79(1). 19–29. 24 indexed citations
12.
Yeh, Jinn‐Yi & Jachih Fu. (2005). DOUBLE SIMULATED ANNEALING FOR FUNCTIONAL MRI ANALYSIS. Journal of the Chinese Institute of Industrial Engineers. 22(6). 497–508. 3 indexed citations
13.
Wong, Stephen T.C., et al.. (2004). A neuroinformatics database system for disease-oriented neuroimaging research. Academic Radiology. 11(3). 345–358. 17 indexed citations
14.
Cao, Xinhua, et al.. (2004). A Web-Based Federated Neuroinformatics Model for Surgical Planning and Clinical Research Applications in Epilepsy. Neuroinformatics. 2(1). 101–118. 4 indexed citations
15.
Fu, Jachih, et al.. (2002). De-noising of left ventricular myocardial borders in magnetic resonance images. Magnetic Resonance Imaging. 20(9). 649–657. 4 indexed citations
16.
Fu, Jachih, Jyh‐Wen Chai, & Stephen T.C. Wong. (2000). Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic resonance images. Magnetic Resonance Imaging. 18(9). 1135–1141. 20 indexed citations
17.
Fu, Jachih, Han‐Chung Lien, & Stephen T.C. Wong. (2000). Wavelet-based histogram equalization enhancement of gastric sonogram images. Computerized Medical Imaging and Graphics. 24(2). 59–68. 30 indexed citations
18.
19.
Fu, Jachih, et al.. (1996). Chatter classification by entropy functions and morphological processing in cylindrical traverse grinding. Precision Engineering. 18(2-3). 110–117. 10 indexed citations
20.
Fu, Jachih, et al.. (1994). Application of entropy functions in on-line vibration classification for cylindrical plunge grinding. International Journal of Production Research. 32(6). 1477–1487. 4 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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