Dr. Md. Mahmudur Rahman Profile

Dr. Md. Mahmudur Rahman

Assistant Professor at Morgan State Univ

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Publications (7)

Proceedings Article | 3 March 2017 Paper

Automated melanoma recognition in dermoscopic images based on extreme learning machine (ELM)

Md. Mahmudur Rahman, Nuh Alpaslan

Proceedings Volume 10134, 1013414 (2017) https://doi.org/10.1117/12.2255576

KEYWORDS: Melanoma, CAD systems, Skin cancer, Feature extraction, Computer aided diagnosis and therapy, Diagnostics, In vivo imaging, Image segmentation, Image classification, Computing systems

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SPIE Journal Paper | 30 December 2015

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval

Md. Mahmudur Rahman, Sameer Antani, Dina Demner-Fushman, George Thoma

JMI, Vol. 2, Issue 04, 046502, (December 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.4.046502

KEYWORDS: Image retrieval, Visualization, Medical imaging, Biomedical optics, Information visualization, Feature extraction, Computed tomography, Image processing, Databases, Lung

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Proceedings Article | 17 March 2015 Paper

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

Md Rahman, Sameer Antani, Dina Demner-Fushman, George Thoma

Proceedings Volume 9418, 94180U (2015) https://doi.org/10.1117/12.2081456

KEYWORDS: Image retrieval, Visualization, Medical imaging, Biomedical optics, Feature extraction, Information visualization, Computed tomography, Image processing, Lung, Image segmentation

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Proceedings Article | 19 March 2014 Paper

Biomedical image representation and classification using an entropy weighted probabilistic concept feature space

Md Mahmudur Rahman, Sameer Antani, Dinna Demner-Fushman, George Thoma

Proceedings Volume 9039, 903908 (2014) https://doi.org/10.1117/12.2043911

KEYWORDS: Medical imaging, Visualization, Image classification, Image segmentation, Image retrieval, Biomedical optics, Feature extraction, Classification systems, Image information entropy, Image quality

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Proceedings Article | 29 March 2013 Paper

Text- and content-based biomedical image modality classification

Daekeun You, Md Mahmudur Rahman, Sameer Antani, Dina Demner-Fushman, George Thoma

Proceedings Volume 8674, 86740L (2013) https://doi.org/10.1117/12.2007932

KEYWORDS: Visualization, Feature extraction, Image retrieval, Biomedical optics, Medical imaging, Feature selection, Image classification, Radiology, X-rays, Ultrasonography

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Proceedings Article | 24 January 2011 Paper

Automatic identification of ROI in figure images toward improving hybrid (text and image) biomedical document retrieval

Daekeun You, Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju, George Thoma

Proceedings Volume 7874, 78740K (2011) https://doi.org/10.1117/12.873434

KEYWORDS: Image retrieval, Biomedical optics, Feature extraction, Image segmentation, Visualization, Edge detection, Magnetorheological finishing, Ions, Neodymium, Medicine

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Proceedings Article | 18 January 2010 Paper

Biomedical article retrieval using multimodal features and image annotations in region-based CBIR

Daekeun You, Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju, George Thoma

Proceedings Volume 7534, 75340V (2010) https://doi.org/10.1117/12.838973

KEYWORDS: Image retrieval, Biomedical optics, Image segmentation, Feature extraction, Magnetorheological finishing, Detection and tracking algorithms, Medicine, Medical imaging, Image processing, Visualization

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Biomedical images are invaluable in establishing diagnosis, acquiring technical skills, and implementing best practices in many areas of medicine. At present, images needed for instructional purposes or in support of clinical decisions appear in specialized databases and in biomedical articles, and are often not easily accessible to retrieval tools. Our goal is to automatically annotate images extracted from scientific publications with respect to their usefulness for clinical decision support and instructional purposes, and project the annotations onto images stored in databases by linking images through content-based image similarity. Authors often use text labels and pointers overlaid on figures and illustrations in the articles to highlight regions of interest (ROI). These annotations are then referenced in the caption text or figure citations in the article text. In previous research we have developed two methods (a heuristic and dynamic time warping-based methods) for localizing and recognizing such pointers on biomedical images. In this work, we add robustness to our previous efforts by using a machine learning based approach to localizing and recognizing the pointers. Identifying these can assist in extracting relevant image content at regions within the image that are likely to be highly relevant to the discussion in the article text. Image regions can then be annotated using biomedical concepts from extracted snippets of text pertaining to images in scientific biomedical articles that are identified using National Library of Medicine's Unified Medical Language System® (UMLS) Metathesaurus. The resulting regional annotation and extracted image content are then used as indices for biomedical article retrieval using the multimodal features and region-based content-based image retrieval (CBIR) techniques. The hypothesis that such an approach would improve biomedical document retrieval is validated through experiments on an expert-marked biomedical article dataset.

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