Dr. Ling Ma Profile

Dr. Ling Ma

Research Associate at Univ of Texas at Dallas

SPIE Involvement:

Author

Area of Expertise:

Hyperspectral imaging , Deep learning , Machine learning , Instrumentation , Optical design , Spectroscopy

Publications (31)

SPIE Journal Paper | 13 August 2024 Open Access

Design and validation of a high-speed hyperspectral laparoscopic imaging system

Kelden Pruitt, Ling Ma, Armand Rathgeb, Jeffrey Gahan, Brett Johnson, Douglas Strand, Baowei Fei

JBO, Vol. 29, Issue 09, 093506, (August 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.9.093506

KEYWORDS: Cameras, Imaging systems, Laparoscopy, Tissues, Reflectivity, Hyperspectral imaging, Near infrared, Spatial resolution, Design, Tunable filters

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SignificanceMinimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS.AimWe present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system’s accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera.ApproachStandard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues.ResultsMultiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600 nm), red/NIR (RNIR) (610 to 850 nm), and NIR (665 to 950 nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of 3.51±2.03%, 3.43±0.84%, and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques.ConclusionsWe developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

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SPIE Journal Paper | 24 July 2024 Open Access

Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks

Minh Ha Tran, Ling Ma, Hasan Mubarak, Ofelia Gomez, James Yu, Michelle Bryarly, Baowei Fei

JBO, Vol. 29, Issue 09, 093505, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.9.093505

KEYWORDS: Thyroid, Education and training, RGB color model, Hyperspectral imaging, Cancer detection, Cancer, Transformers, Machine learning, Image classification, Visualization

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SignificanceHyperspectral imaging (HSI) is an emerging imaging modality for oncological applications and can improve cancer detection with digital pathology.AimThe study aims to highlight the increased accuracy and sensitivity of detecting the margin of thyroid carcinoma in hematoxylin and eosin (H&E)-stained histological slides using HSI and data augmentation methods.ApproachUsing an automated microscopic imaging system, we captured 2599 hyperspectral images from 65 H&E-stained human thyroid slides. Images were then preprocessed into 153,906 image patches of dimension 250×250×84 pixels. We modified the TimeSformer network architecture, which used alternating spectral attention and spatial attention layers. We implemented several data augmentation methods for HSI based on the RandAugment algorithm. We compared the performances of TimeSformer on HSI against the performances of pretrained ConvNext and pretrained vision transformers (ViT) networks on red, green, and blue (RGB) images. Finally, we applied attention unrolling techniques on the trained TimeSformer network to identify the biological features to which the network paid attention.ResultsIn the testing dataset, TimeSformer achieved an accuracy of 90.87%, a weighted F1 score of 89.79%, a sensitivity of 91.50%, and an area under the receiving operator characteristic curve (AU-ROC) score of 97.04%. Additionally, TimeSformer produced thyroid carcinoma tumor margins with an average Jaccard score of 0.76 mm. Without data augmentation, TimeSformer achieved an accuracy of 88.23%, a weighted F1 score of 86.46%, a sensitivity of 85.53%, and an AU-ROC score of 94.94%. In comparison, the ViT network achieved an 89.98% accuracy, an 88.14% weighted F1 score, an 84.77% sensitivity, and a 96.17% AU-ROC. Our visualization results showed that the network paid attention to biological features.ConclusionsThe TimeSformer model trained with hyperspectral histological data consistently outperformed conventional RGB-based models, highlighting the superiority of HSI in this context. Our proposed augmentation methods improved the accuracy, the F1 score, and the sensitivity score.

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Proceedings Article | 2 April 2024 Poster + Paper

Nerve detection and visualization using hyperspectral imaging for surgical guidance

Minh Ha Tran, Michelle Bryarly, Ling Ma, Muhammad Saad Yousuf, Theodore Price, Baowei Fei

Proceedings Volume 12930, 129302A (2024) https://doi.org/10.1117/12.3008470

KEYWORDS: Nerve, Nervous system, Image segmentation, Adipose tissue, RGB color model, Hyperspectral imaging, Surgery, Visualization

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Proceedings Article | 2 April 2024 Presentation + Paper

A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance

Ling Ma, Kelden Pruitt, Baowei Fei

Proceedings Volume 12930, 129300Z (2024) https://doi.org/10.1117/12.3008789

KEYWORDS: Hyperspectral imaging, Cameras, Super resolution, Microscopes, Image restoration, Tissues, Image acquisition, Image quality, Medical image reconstruction, Convolutional neural networks, Deep learning

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Proceedings Article | 1 April 2024 Poster + Paper

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Teja Pathour, Ling Ma, Douglas Strand, Jeffrey Gahan, Brett Johnson, Shashank Sirsi, Baowei Fei

Proceedings Volume 12932, 129321F (2024) https://doi.org/10.1117/12.3008643

KEYWORDS: Prostate, Ultrasonography, Radiomics, Feature extraction, Tissues, Tunable filters, Image segmentation, Prostate cancer, Image filtering, Acoustics

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Proceedings Article | 29 March 2024 Presentation + Paper

Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance

Ling Ma, Kelden Pruitt, Baowei Fei

Proceedings Volume 12928, 129280I (2024) https://doi.org/10.1117/12.3006573

KEYWORDS: Hyperspectral imaging, Cameras, Laparoscopy, Super resolution, Image restoration, Tissues, Imaging systems, Medical image reconstruction, Convolutional neural networks, Deep learning

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Proceedings Article | 13 March 2024 Presentation + Paper

A high-resolution hyperspectral imaging system for the retina

Minh Ha Tran, Michelle Bryarly, Kelden Pruitt, Ling Ma, Baowei Fei

Proceedings Volume 12836, 1283604 (2024) https://doi.org/10.1117/12.3001647

KEYWORDS: Hyperspectral imaging, Retina, Imaging systems, Cameras, Retinal scanning, Tunable filters

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SPIE Journal Paper | 18 January 2024 Open Access

Polarized hyperspectral microscopic imaging system for enhancing the visualization of collagen fibers and head and neck squamous cell carcinoma

Ximing Zhou, Ling Ma, Hasan Mubarak, Doreen Palsgrove, Baran Sumer, Amy Chen, Baowei Fei

JBO, Vol. 29, Issue 01, 016005, (January 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.1.016005

KEYWORDS: Tumors, Collagen, RGB color model, Hyperspectral imaging, Tissues, Imaging systems, Visualization, Microscopes, Neck, Head

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SignificancePolarized hyperspectral microscopes with the capability of full Stokes vector imaging have potential for many biological and medical applications.AimThe aim of this study is to investigate polarized hyperspectral imaging (PHSI) for improving the visualization of collagen fibers, which is an important biomarker related to tumor development, and improving the differentiation of normal and tumor cells on pathologic slides.ApproachWe customized a polarized hyperspectral microscopic imaging system comprising an upright microscope with a motorized stage, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a compact SnapScan hyperspectral camera. The polarizers and LCVRs worked in tandem with the hyperspectral camera to acquire polarized hyperspectral images, which were further used to calculate four Stokes vectors: S0, S1, S2, and S3. Synthetic RGB images of the Stokes vectors were generated for the visualization of cellular components in PHSI images. Regions of interest of collagen, normal cells, and tumor cells in the synthetic RGB images were selected, and spectral signatures of the selected components were extracted for comparison. Specifically, we qualitatively and quantitatively investigated the enhanced visualization and spectral characteristics of dense fibers and sparse fibers in normal stroma tissue, fibers accumulated within tumors, and fibers accumulated around tumors.ResultsBy employing our customized polarized hyperspectral microscope, we extract the spectral signatures of Stokes vector parameters of collagen as well as of tumor and normal cells. The measurement of Stokes vector parameters increased the image contrast of collagen fibers and cells in the slides.ConclusionsWith the spatial and spectral information from the Stokes vector data cubes (S0, S1, S2, and S3), our PHSI microscope system enhances the visualization of tumor cells and tumor microenvironment components, thus being beneficial for pathology and oncology.

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Proceedings Article | 7 April 2023 Presentation + Paper

Extended depth of field imaging for mosaic hyperspectral images

Armand Rathgeb, Ling Ma, Minh Tran, Baowei Fei

Proceedings Volume 12471, 1247109 (2023) https://doi.org/10.1117/12.2653923

KEYWORDS: Hyperspectral imaging, Image quality, Image sharpness, Image restoration, Microscopes

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Proceedings Article | 7 April 2023 Poster + Paper

Conditional generative adversarial network (cGAN) for synthesis of digital histologic images from hyperspectral images

Ling Ma, Jeremy Sherey, Doreen Palsgrove, Baowei Fei

Proceedings Volume 12471, 124711D (2023) https://doi.org/10.1117/12.2653715

KEYWORDS: Hyperspectral imaging, RGB color model, Tissues, Education and training, Visualization, Tumors, Image quality, Imaging systems, Cameras, Pathology

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Proceedings Article | 3 April 2023 Presentation + Paper

A high-speed hyperspectral laparoscopic imaging system

Kelden Pruitt, Brett Johnson, Jeffrey Gahan, Ling Ma, Baowei Fei

Proceedings Volume 12466, 1246608 (2023) https://doi.org/10.1117/12.2653922

KEYWORDS: Tissues, Imaging systems, Reflectivity, Laparoscopy, Hyperspectral imaging, Cameras, Spatial resolution, Calibration, Liver, Kidney

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Proceedings Article | 16 March 2023 Presentation + Paper

Automated polarized hyperspectral imaging (PHSI) for ex-vivo and in-vivo tissue assessment

Ling Ma, Akhila Srinivas, Abirami Krishnamurthy, Ximing Zhou, Nimit Subhashbhai Shah, Girgis Obaid, Baowei Fei

Proceedings Volume 12391, 123910F (2023) https://doi.org/10.1117/12.2651011

KEYWORDS: Tissues, Polarization, Liver, Kidney, Spleen, Lung, Muscles, Hyperspectral imaging, In vivo imaging, Polarized light

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Proceedings Article | 15 March 2023 Presentation + Paper

Polarized hyperspectral microscopic imaging for collagen visualization on pathologic slides of head and neck squamous cell carcinoma

Ximing Zhou, Hasan Mubarak, Ling Ma, Doreen Palsgrove, Baran Summer, Baowei Fei

Proceedings Volume 12382, 1238204 (2023) https://doi.org/10.1117/12.2655831

KEYWORDS: Collagen, Tumors, RGB color model, Hyperspectral imaging, Microscopes, Visualization, Imaging systems, Polarizers

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Proceedings Article | 15 March 2023 Presentation + Paper

Polarized hyperspectral microscopic imaging for white blood cells on Wright-stained blood smear slides

Ximing Zhou, Hasan Mubarak, Ling Ma, Doreen Palsgrove, Samuel Ortega, Gustavo Marrero Callicó, Edward Medina, Bradley Brimhall, Marisa Whitted, Baowei Fei

Proceedings Volume 12382, 1238207 (2023) https://doi.org/10.1117/12.2655708

KEYWORDS: White blood cells, RGB color model, Polarized light, Hyperspectral imaging, Biological imaging, Blood, Imaging systems, Visualization, Polarization, Microscopes

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White blood cells, also called leukocytes, are hematopoietic cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. The abnormal development and uncontrolled proliferation of these cells can lead to devastating cancers. Their timely recognition in the peripheral blood is critical to diagnosis and treatment. In this study, we developed a microscopic imaging system for improving the visualization of white blood cells on Wright’s stained blood smear slides, with two different setups: polarized light imaging and polarized hyperspectral imaging. Based on the polarized light imaging setup, we collected the RGB images of Stokes vector parameters (S0, S1, S2, and S3) of five types of white blood cells (neutrophil, eosinophil, basophil, lymphocyte, and monocyte), and calculated the Stokes vector derived parameters: the degree of polarization (DOP), the degree of linear polarization (DOLP), and the degree of circular polarization (DOCP). Based on the polarized hyperspectral imaging setup, we also calculated Stokes vector data. The preliminary results demonstrate that Stokes vector derived parameters (DOP, DOLP, and DOCP) could improve the visualization of granules in granulocytes (neutrophils, eosinophils, and basophils). Furthermore, Stokes vector derived parameters (DOP, DOLP, and DOCP) could improve the visualization of surface structures (protein patterns) of lymphocytes enabling subclassification of lymphocyte subpopulations. Finally, S2, S3, and DOCP could improve the visualization of morphology on nucleus of monocytes. We also demonstrated that the polarized hyperspectral imaging setup could provide complementary spectral information to the spatial information on different Stokes vector parameters of white blood cells. This work demonstrates that polarized light imaging and polarized hyperspectral imaging has the potential to become a strong imaging tool in the diagnosis of disorders arising from white blood cells.

SPIE Journal Paper | 28 May 2022 Open Access

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging (Erratum)

Ling Ma, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

JBO, Vol. 27, Issue 05, 059802, (May 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.5.059802

KEYWORDS: Neck, Hyperspectral imaging, Head, Medicine, Error analysis, Radiology, Precision measurement, Pathology, Microscopy, Medical research

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SPIE Journal Paper | 23 May 2022 Open Access

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging (Errata)

Ling Ma, Armand Rathgeb, Hasan Mubarak, Minh Tran, Baowei Fei

JBO, Vol. 27, Issue 05, 059801, (May 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.5.059801

KEYWORDS: Super resolution, Hyperspectral imaging, Radiology, Precision measurement, Machine vision, Image classification, Computer vision technology

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SPIE Journal Paper | 16 May 2022 Open Access

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging

Ling Ma, Armand Rathgeb, Hasan Mubarak, Minh Tran, Baowei Fei

JBO, Vol. 27, Issue 05, 056502, (May 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.5.056502

KEYWORDS: Hyperspectral imaging, Super resolution, RGB color model, Image classification, Image quality, Cameras, Tissues, Cancer, Image resolution, Tumors

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SPIE Journal Paper | 29 April 2022 Open Access

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Ling Ma, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

JBO, Vol. 27, Issue 04, 046501, (April 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.4.046501

KEYWORDS: RGB color model, Hyperspectral imaging, Image segmentation, Cancer, Image classification, Tissues, Neck, Head, Cameras, Imaging systems

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Significance: Automatic, fast, and accurate identification of cancer on histologic slides has many applications in oncologic pathology. Aim: The purpose of this study is to investigate hyperspectral imaging (HSI) for automatic detection of head and neck cancer nuclei in histologic slides, as well as cancer region identification based on nuclei detection. Approach: A customized hyperspectral microscopic imaging system was developed and used to scan histologic slides from 20 patients with squamous cell carcinoma (SCC). Hyperspectral images and red, green, and blue (RGB) images of the histologic slides with the same field of view were obtained and registered. A principal component analysis-based nuclei segmentation method was developed to extract nuclei patches from the hyperspectral images and the coregistered RGB images. Spectra-based support vector machine and patch-based convolutional neural networks (CNNs) were implemented for nuclei classification. The CNNs were trained with RGB patches (RGB-CNN) and hyperspectral patches (HSI-CNN) of the segmented nuclei and the utility of the extra spectral information provided by HSI was evaluated. Furthermore, cancer region identification was implemented by image-wise classification based on the percentage of cancerous nuclei detected in each image. Results: RGB-CNN, which mainly used the spatial information of nuclei, resulted in a 0.81 validation accuracy and 0.74 testing accuracy. HSI-CNN, which utilized the spatial and spectral features of the nuclei, showed significant improvement in classification performance and achieved 0.89 validation accuracy as well as 0.82 testing accuracy. Furthermore, the image-wise cancer region identification based on nuclei detection could generally improve the cancer detection rate. Conclusions: We demonstrated that the morphological and spectral information contribute to SCC nuclei differentiation and that the spectral information within hyperspectral images could improve classification performance.

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Proceedings Article | 4 April 2022 Presentation + Paper

Unsupervised super resolution network for hyperspectral histologic imaging

Ling Ma, Armand Rathgeb, Minh Tran, Baowei Fei

Proceedings Volume 12039, 120390P (2022) https://doi.org/10.1117/12.2611889

KEYWORDS: Hyperspectral imaging, RGB color model, Super resolution, Cameras, Image quality, Multispectral imaging, Cancer, Pathology, Microscopy, Microscopes

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Proceedings Article | 4 April 2022 Presentation + Paper

Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning

Ximing Zhou, Ling Ma, Hasan Mubarak, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

Proceedings Volume 12039, 120390G (2022) https://doi.org/10.1117/12.2614624

KEYWORDS: RGB color model, Hyperspectral imaging, Tissues, Neck, Head, Cancer, Microscopes, Imaging systems, Tumors, Polarization

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Proceedings Article | 4 April 2022 Presentation + Paper

Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning

Minh Ha Tran, Ling Ma, James Litter, Amy Chen, Baowei Fei

Proceedings Volume 12039, 120390H (2022) https://doi.org/10.1117/12.2612963

KEYWORDS: Hyperspectral imaging, Cancer, Tumors, Network architectures, Tissues, Cameras, Image classification

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Proceedings Article | 4 April 2022 Poster + Paper

Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points

Ted Shi, Maysam Shahedi, Kayla Caughlin, James Dormer, Ling Ma, Baowei Fei

Proceedings Volume 12034, 120341W (2022) https://doi.org/10.1117/12.2611594

KEYWORDS: Image segmentation, Computed tomography, 3D image processing, 3D modeling, Data modeling, Heart, Magnetic resonance imaging, Convolutional neural networks, Prostate, Lithium

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Proceedings Article | 2 March 2022 Presentation + Paper

LED-based hyperspectral endoscopic imaging

Naeeme Modir, Maysam Shahedi, James Dormer, Ling Ma, Mohammadaref Ghaderi, Shashank Sirsi, Yi-Shing Lisa Cheng, Baowei Fei

Proceedings Volume 11954, 1195408 (2022) https://doi.org/10.1117/12.2609023

KEYWORDS: Imaging systems, Cameras, Tissues, Light emitting diodes, Hyperspectral imaging, Prototyping, Calibration, Endoscopy, Cancer, Endoscopes

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Proceedings Article | 15 February 2021 Presentation + Paper

Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning

Ximing Zhou, Ling Ma, William Brown, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

Proceedings Volume 11603, 116030Q (2021) https://doi.org/10.1117/12.2582330

KEYWORDS: Hyperspectral imaging, Tissues, Machine learning, Neck, Head, RGB color model, Principal component analysis, Microscopes, Pathology, Image classification

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Proceedings Article | 15 February 2021 Presentation + Paper

Pixel-level tumor margin assessment of surgical specimen with hyperspectral imaging and deep learning classification

Ling Ma, Maysam Shahedi, Ted Shi, Martin Halicek, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

Proceedings Volume 11598, 1159811 (2021) https://doi.org/10.1117/12.2581046

KEYWORDS: Tumors, Hyperspectral imaging, Image classification, Tissues, Cancer, Surgery, Optical imaging, In vivo imaging, Convolutional neural networks

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Proceedings Article | 15 February 2021 Presentation + Paper

Hyperspectral microscopic imaging for the detection of head and neck squamous cell carcinoma on histologic slides

Ling Ma, Ximing Zhou, James Little, Amy Chen, Larry Myers, Baran Sumer, Baowei Fei

Proceedings Volume 11603, 116030P (2021) https://doi.org/10.1117/12.2581970

KEYWORDS: Hyperspectral imaging, Neck, Head, Convolutional neural networks

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SPIE Journal Paper | 4 January 2021 Open Access

Comprehensive review of surgical microscopes: technology development and medical applications

Ling Ma, Baowei Fei

JBO, Vol. 26, Issue 01, 010901, (January 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.1.010901

KEYWORDS: Microscopes, Surgery, Visualization, Tumors, Luminescence, Imaging systems, Optical coherence tomography, Augmented reality, Cameras, Tissues

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Significance: Surgical microscopes provide adjustable magnification, bright illumination, and clear visualization of the surgical field and have been increasingly used in operating rooms. State-of-the-art surgical microscopes are integrated with various imaging modalities, such as optical coherence tomography (OCT), fluorescence imaging, and augmented reality (AR) for image-guided surgery. Aim: This comprehensive review is based on the literature of over 500 papers that cover the technology development and applications of surgical microscopy over the past century. The aim of this review is threefold: (i) providing a comprehensive technical overview of surgical microscopes, (ii) providing critical references for microscope selection and system development, and (iii) providing an overview of various medical applications. Approach: More than 500 references were collected and reviewed. A timeline of important milestones during the evolution of surgical microscope is provided in this study. An in-depth technical overview of the optical system, mechanical system, illumination, visualization, and integration with advanced imaging modalities is provided. Various medical applications of surgical microscopes in neurosurgery and spine surgery, ophthalmic surgery, ear-nose-throat (ENT) surgery, endodontics, and plastic and reconstructive surgery are described. Results: Surgical microscopy has been significantly advanced in the technical aspects of high-end optics, bright and shadow-free illumination, stable and flexible mechanical design, and versatile visualization. New imaging modalities, such as hyperspectral imaging, OCT, fluorescence imaging, photoacoustic microscopy, and laser speckle contrast imaging, are being integrated with surgical microscopes. Advanced visualization and AR are being added to surgical microscopes as new features that are changing clinical practices in the operating room. Conclusions: The combination of new imaging technologies and surgical microscopy will enable surgeons to perform challenging procedures and improve surgical outcomes. With advanced visualization and improved ergonomics, the surgical microscope has become a powerful tool in neurosurgery, spinal, ENT, ophthalmic, plastic and reconstructive surgeries.

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Proceedings Article | 16 March 2020 Presentation + Paper

Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning

Ling Ma, Martin Halicek, Ximing Zhou, James Dormer, Baowei Fei

Proceedings Volume 11320, 113200W (2020) https://doi.org/10.1117/12.2549369

KEYWORDS: RGB color model, Hyperspectral imaging, Cancer, Image segmentation, Head, Neck, Tissues, Imaging systems, Machine learning, Principal component analysis

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Proceedings Article | 28 February 2020 Presentation + Paper

In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction

Ling Ma, Martin Halicek, Baowei Fei

Proceedings Volume 11317, 113171C (2020) https://doi.org/10.1117/12.2549397

KEYWORDS: Wavelets, Discrete wavelet transforms, Hyperspectral imaging, Tissues, Feature extraction, Cancer, Tumors, In vivo imaging, Animal model studies, Head

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Proceedings Article | 19 February 2020 Presentation + Paper

Development of a new polarized hyperspectral imaging microscope

Ximing Zhou, Ling Ma, Martin Halicek, James Dormer, Baowei Fei

Proceedings Volume 11213, 1121308 (2020) https://doi.org/10.1117/12.2549676

KEYWORDS: Hyperspectral imaging, Tissues, Cancer, Microscopes, Imaging systems, Polarization, Head, Neck, Optical testing

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SPIE Journal Paper | 28 August 2017

Robust method for interest region description based on local intensity binary pattern

Yi Yang, Fajie Duan, Jiajia Jiang, Ling Ma, Hao Zheng

JEI, Vol. 26, Issue 04, 043025, (August 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.4.043025

KEYWORDS: Binary data, Image compression, 3D image processing, Object recognition, Sensors, Error analysis, Principal component analysis, Image analysis, Zoom lenses, Machine vision

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