Biomedical Image & Data Analysis Lab - BIDAL








We are interested in developing an intuitive computational framework to better analyze medical and biological data in order to innovate the way such data are used in the current clinical practice and scientific research. Our research explores various computational theories for developing new robust, efficient and/or large-scale algorithms and seeks their practical applications to solve clinically and scientifically significant problems, such as cancer prevention. BIDAL is located in San Francisco State University and affiliated with SFSU’s Center for Computing for Life Sciences.




Prof. Okada presented Keynote Talk at College of Science and Engineering Showcase Event, San Francisco State University, May 2017

Professors team up to design a better prosthetic arm, SF State News, Mar 2016

Computer technology aids medical advances, SF State News, Feb 2015




Kazunori Okada

Principal Investigator, Professor, CS Dept. SFSU

Ph.D. in Computer Science at University of Southern California

Links: Home, Research, kazokada AT

Xinwei Fan (MSc Student, CS Dept, SFSU: Jan/2021 – Current)

Project: A Driver Navigation Assistant with Lane Detection

Links: xfan4 AT

Luis Fernando Chumpitaz Diaz (MSc Student, CS Dept, SFSU: Aug/2020 – Current)

Project: Categorizing the Three-Dimensional Compartmentalization of the Genome

Links: lchumpit AT

Jakob Dohrmann (MSc Student, CS Dept, SFSU: Apr/2020 – Dec/2020)

Project: Optimizing a Prediction Pipeline by Prepending an Efficient Low-Fidelity Model

Links: jakobd AT


Katie Fotion (MSc, May 2018) currently at Cruise

Jeff Hung (MSc, May 2018) currently at Genentech, Inc

Saman Porhemmat (BSc, Feb 2018) currently at UC Irvine

Maya Stark (MSc, Feb 2018) currently at Genentech, Inc

Juniper Overbeck (BSc MATH, Dec 2017) currently at SFSU

Andre Simpelo (BSc, May 2017) currently at Udacity

Octavian Drulea (MSc, May 2017) currently at Driver

Juris Puchin (MSc, May 2017) currently at Acuity Brands

Rupal Khilari (MSc, May 2017) currently at The Mill

Serge Dusheyko (MSc EECS, May 2017) currently at Colorado Electronic Product Design, Inc

Diana Chu (BEng, Dec 2016) currently at Cornell University

Poulomi Das (BSc, May 2016) currently at UCSF Medical Center

Nao Funada (BSc, May 2015) currently at Softbank Corp

Marzieh Golbaz (MSc, May 2015) currently at Evidera

Sergey Sergeev (MSc, May 2013) currently at SquareTrade

John Collins (MSc, May 2013) currently at Oregon Child Development Coalition

Oliver Newland (MSc, May 2013) currently at CrowdFlower

Eric Chiang (BS, Jan 2013) currently at CoreOS

Hong Ge (Visiting Professor: Jan 2012- Jan 2013) South China Normal University, China

Malik Fahem (Visiting Student: Jul 2012 – Aug 2012) ENSEEIHT, Toulouse, France

Jing Huo (PhD, 2012) GBM Tumor Analysis, UCLA, currently at TeraRecon Inc

Asha Rani Hongenalli Chandrappa (MSc, 2012) RTI Project, currently at Assurant

Miyuki Suzuki (MSc, Dec 2011) currently at Capital One

Runtang Wang (MSc, Feb 2011) currently at The Mind Research Network

Niranjan Timilsina (MSc, Sep 2010) currently at Trulia

Yang Zhao (MSc, May 2010) currently at GoDaddy

Andrew Scott (BS, May 2010) currently at SFSU

Ngoc Lam-Miller (MSc, Oct 2009) currently at EMC/Greenplum

Xinhang Shao (MSc, Oct 2009) currently at Silicon Valley Education Foundation

Mehran Kafai (MSc, May 2009) currently at

Yiyi Miao (MSc, Feb 2009) currently at OPSWAT

Rodrigo Salas (MSc, Feb 2009) currently at Intel Corporation

David Wang (BS, Dec 2008) currently at CSU East Bay

Edgar Liu (MSc, May 2008) currently at Acer

Arturo Flores (MSc, May 2008) currently at KAB Laboratories

Steven Rysavy (MSc, May 2008) currently at Pacific Northwest National Laboratory

Sean Todd (BS, May 2008) currently at Winasaurus

Ryan West (BS, Aug 2007) currently at Location Labs
Taeil Goh
(BS, Dec 2007) currently at OPSWAT


Selected current and previous projects are summarized in the below table.


Development of the Next-Generation Myoelectric Controlled Prosthetic Arms: There are over 32 million amputees worldwide whose lives are severely impacted by their limb losses. With the advances of electromyography (EMG) signal processing, myoelectric controlled prosthetic arm, which deciphers movement intentions from EMG signals using pattern recognition algorithms to control prosthetic arms, has shown great potential for intuitive prosthesis control. However, the clinical and commercial impact of existing myoelectric controlled prosthetic arms are still limited because natural limb movements are usually continuous and dynamic; however, existing myoelectric control interfaces based on standard single-channel EMG recordings can only provide discrete decisions of static motions due to the lack of meaningful neuromuscular information can be captured. Our research aims to address this challenge and develop the next-generation myoelectric controlled arms by integrating grid sensing, machine learning, and neuromorphic computing technologies. Supported by 2016 Ken Fong Translational Research Fund, SFSU Center for Computing for Life Sciences (CCLS) Mini Grant. News, Publications:

Image result for myo band

MyoHMI: A Low-Cost and Flexible Platform for Developing Real-Time Human Machine Interface for Myoelectric Controlled Applications: Existing research platforms for developing EMG pattern recognition algorithms are typically based on MATLAB and the collection of EMG signals is often done by expensive, non-portable data acquisition systems. The requirement of these resources usually limits the use of these platforms in the lab environments and prohibits their widespread to other fields and applications. To address this limitation, this project aims to develop a low-cost, easy to use, and flexible platform called MyoHMI for developing real-time human machine interfaces for myoelectric controlled applications. MyoHMI facilitates the interface with a commercial EMG-based armband Myo, which costs less than $200 and can be easily worn by the user without the need of special preparation. MyoHMI also provides a highly modular and customizable C/C++ based software engine which seamlessly integrates a variety of interfacing and signal processing modules, from data acquisition through signal processing and pattern recognition, to real-time evaluation and control. Video, Publications: SMC16, ASEEPSW17, EMBC17

Digital Facial Dysmorphology for Genetic Screening: Summary: To address the lack of safe, reliable, accessible, and economical screening for genetic diseases, such as Down syndrome, in infants after their birth, we have developed a non-invasive protocol that automatically analyzes and classifies infant’s facial images. Techniques such as independent component analysis (ICA) and constrained local models (CLM) are adapted to yield 96% accuracy, superior to the accuracy by human experts , Investigators: Okada (Collaborators, Drs. Zhao & Linguraru, CNMC), Publications: EMBC13, MICCAI13, MIA14, EMBC14, ISBI15, US9443132

Lung Biomarkers for Children: Summary: Viral infection is a leading cause of death for children worldwide. Common diagnostic tests for adults, such as PFT and CT scans, are ineffective for pediatric population due to their inability to follow oral instructions and high radiation risk. We propose a diagnostic measure to quantify severity of lung infection by assessing inhomogeneity of lung fields due to air trapping by analyzing X-ray images of children, Investigators: Golbaz, Okada (Collaborators, Drs. Nino & Linguraru, CNMC), Publications: IJBI13, CARS15, EMBC15, ATSIC16

Protein Crystallization Image Classification with Class-Imbalanced Datasets: Summary: Protein crystallization plays a crucial role in pharmaceutical research by supporting the investigation of a protein’s molecular structure through X-ray diffraction of its crystal. Due to the rare occurrence of crystals, images must be manually inspected, a laborious process. We develop a solution incorporating a regularized, logistic regression model for automatically evaluating these images. Our particular focus is to investigate how best to train a ML classifier in the presence of severe class-skews in datasets. Investigators: Hung, Weldetsion, Newland, Chiang, Collins, Okada (Collaborators, Genentech, Inc.), Publications: SPIE14

Protein Function Analysis with Microenvironments by Random Forest: Summary: Machine learning-based prediction of protein functions plays a key role in bioinformatics and pharmaceutical research, facilitating swift discovery of new drugs in high-throughput settings. We investigate an adaptation of Random Forest to the structure-based protein function prediction. Our system represents protein’s 3D physicochemical structural information in microenvironment descriptors whose spatial resolution is much finer than other sequence-based protein descriptors. Variable importance measures of RF are also investigated as a tool for mining important genes for particular functional class. Investigators: Okada (Collaborators, Dr. Altman, Stanford, Dr. Petkovic, SFSU), Publications: ICPR14

Variable Interaction Analysis with Random Forest Classifiers: Summary: variable interaction quantifies the extent of complex statistical interaction between a pair of variables beyond linear correlation. We explored such a measure in a supervised classification setting using the well-known Random Forest.  We propose 1) an explicit formula for the Gini-based variable interaction originally proposed by Breiman, as well as a novel permutation-based variable interaction extending the well-known permutation-based variable importance measure of RF. Investigators: Hong, Okada, Publications: ISBI12

Assessment and Prediction of Teamwork Effectiveness in Software Engineering Courses: Summary: One of the challenges in effective software engineering (SE) education is the lack of objective assessment methods of how well student teams learn the critically needed teamwork practices. We propose a novel machine learning approach to quantitatively assess and predict student learning of teamwork effectiveness in software engineering education based on: a) extracting only objective and quantitative student team activity data during their team class project; b) pairing these data with related independent observations and grading of student team effectiveness in SE process and SE product components. Investigators: Okada, (Collaborators, Dr Petkovic, SFSU, Dr. Huang FAU, Dr. Todtenhoefer, UAS Fulda), Publications: FIE12, ACMSIGSOFT13, FIE14, FIE16

Multi Organ Segmentation with Missing Organs in Abdominal CT Images: Summary: In developing a CAD solution for cancers, our target patient population will have a high likelihood of previous surgical organ resection, however most state-of-the-art organ segmentation methods ignore this fact. We propose a statistical method to detect missing organs and simultaneously segment 10 abdominal organs in CT, extending  the atlas-based multi-organ segmentation by Shimizu et al. Investigators: Suzuki, Okada (Collaborators, Dr. Linguraru), Publications: Abdoimg11, MICCAI12

GBM Brain Tumor Segmentation & Classification in Diffusion-Weighted MRI, Summary: Glioblastoma multiforme (GBM) is the most common and most aggressive malignant primary brain tumor in humans. GBM has the worst prognosis of any CNS malignancy. Image-based delineation and diagnosis of GBM remains a significant challenge. We propose a robust Computer-Aided Therapeutic Response (CARrx) paradigm by using the diffusion weighted MRI as a surrogate biomarker to reveal changes in the tumor microenvironment that precedes morphologic tumor changes. To account for the large morphological change of GBM, we propose ensemble segmentation algorithms that combine results from multiple segmentation algorithms as well as perturbations due to user-interaction. Investigators: Huo, Okada, (Collaborator, Dr. Brown, UCLA), Publications: Algorithm09, CDMRI08, SPIE09, SPIE11a, SPIE11b, MLCAD12, MedPhys13, ClinRadiol15

Data/Text Mining for Automatic Lesson Planner for Special Education, Summary: We develop an online web-based tool for assisting teaching credential students in a mild-to-moderate special education program in CA to create sound lesson plans and manage their e-portfolios. Despite tremendous challenges in preparing next generation special education teachers to meet our classroom demands, there are currently no useful tools in practice to assist these teacher candidates to learn how to make sound lesson plans for specific IEPs and regulatory content standards. Our goal is to develop a software tool that automatically suggests some evidence-based teaching strategies that conforms to specific IEP and content-standards through analyzing published peer-reviewed articles by using data mining techniques. Investigators: Sun, Shao, Lam-Miller, (Collaborators, Dr. Courey, SFSU), Publications: CSEDU09, SITE10, EDMEDIA10

Biological 2D Cellular Image Analysis, Summary: This project aims to develop suits of tools to analyze technically challenging biomedical images, such as 2D optical images of immunostained cell nuclei in nervous systems of Tobacco hornworm for studying the effect of starvation in neural development. Our cell counting solution for the Tobacco hornworm images exploits fast radial symmetry transform originally proposed for generic object/face detection. Investigators: Timilsina, (Collaborators, Dr. Moffatt, SFSU), Publications: SPIE11

3D Topology Type Classification in 3D Chest CT, Summary: Correct classification of topological types of local anatomical and pathological structures plays a crucial role in many 3D medical image analysis applications, such as vascular and airway tree analysis, detection of internal bleeding, and false-positive detection for lung nodule detection. We propose a novel algorithm to classify a local 3D structure into four types of topology in 3D (i.e., blob, plane, tube, and branching-tube). The key idea is to transform the analysis of 3D objects into a 2D clustering problem which can be solved efficiently. The proposed algorithm is also extended to Bayesian formalism with a novel mean shift clustering method that accounts for our circular domain data. Investigators: Miao, Kafai, Publications: SPIE06, MLMI10, MMBIA10

Directional Mean Shift, Summary: Mean shift is a convergent hill-climbing algorithm that efficiently seeks a mode of kernel-smoothed function such as kernel density functions and Gaussian-smoothed data. Mean shift has been successfully applied to various vision applications, such as clustering, segmentation, tracking to name a few. Our research extends this mean shift to a wider range of data analysis tasks, including Gaussian scale space, MAP estimation, and directional/circular domain data analysis. Investigators: Kafai, Okada, Publications: MMBIA10

Dental Lesion CAD in 3D Cone Beam CT, Summary: In endodontics, 3D cone beam CT has shown a promise for classifying granulomas (tissue infection) and cysts, which could not be differentiated by traditional X-ray imaging in past, leading to potentially unnecessary surgical removals of these lesions. We propose a new CAD scheme that offers a non-invasive differential diagnosis of the two types of dental lesions to avoid unnecessary tissue damage. Our solutions combine graph-based random walker segmentation and a new LDA-Boost algorithm for diagnostic classification. Investigators: Rysavy, Flores, (Collaborators, Dr. Enciso, USC), Publications: SPIE08, ICPR08, ISBI09, MLMI10, MedPhys15

Lung Nodule Analysis in 3D Chest CT, Summary: Lung cancer accounts for the most cancer death in the US. These cancers often appear as focal concentration of high intensity in CT scans, known as lung nodules. This project aims to provide comprehensive suits of robust and accurate detection, segmentation, and analysis of such nodules for early detection and accurate staging. In particular, our method can robustly segment technically challenging Ground-Glass Opacity (GGO) nodules which pose higher malignancy rate than more common solitary nodules. (Example Results: MICCAI04). Investigators: Okada, Publications: TMI05, ECCV04, CVPR04, CVPR05, MICCAI04, MICCAI05, CVBIA05, KDD06, M3ISR10, CADLI10

Cross-Bin Histogram Distance Measures: EMD_L1 & Diffusion Distance, Summary: Histogram features, such as SIFT and Shape Context, are ubiquitous tools in computer vision and pattern recognition. Earth mover’s distance is a well-known cross-bin metric of such histogram features accounting for ill-aligned histogram bins; however it suffers from high computational cost that is larger than O(n^3). We propose an O(n^2) exact earth mover’s distance algorithm. We simplify the underlying linear program by exploiting the L1 ground distance. We further improve the computation complexity in cross-bin distance computation, proposing Diffusion Distance whose cost is reduced to near O(n), while maintaining high scores on SIFT and shape matching tasks we evaluated. Investigators: Ling, Okada, Publications: PAMI06, CVPR06




-        T. Touati*, K. Okada, I. Song, What makes a person obese?: An individual-level analysis of obesity, Accepted as 1-page extended abstract at IEEE International Conference on Biomedical and Health Informatics, (2021)



-        G. Urban, S. Porhemmat*, M. Stark*, B. Feeley, K. Okada, P. Baldi, Classifying Shoulder Implants in X-ray Images using Deep Learning, Computational Structural Biotechnology Journal, 18: 967-972 (2020)



-        M. Kim*, K. Okada, A. Ryner, A. Amza, Z. Tadesse, S. Cotter, B. Gaynor, J. Keenan, T. Lietman, T. Porco, Sensitivity and specificity of computer vision classification of eyelid photographs for programmatic trachoma assessment, PLoS One, 14(2): e0210463 (2019)

-        A. Mansoor, J. Cerrolaza, G. Perez, E. Biggs, K. Okada, G. Nino, M. Linguraru, A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning, IEEE trans Biomedical Eng., Epub: doi: 10.1109/TBME.2019.2933508 (2019)



-        R. Khilari*, J. Puchin*, K. Okada, Automated quasi-3D spine curvature quantification and classification, In proc. SPIE Medical Imaging, Huston, 2018

-        J. Puchin*, X. Zhang, K. Okada, Independent Component Analysis for Hand Gesture Recognition with EMG Sensor Grid, Submitted to IEEE EMBC, Honolulu, 2018

-        I. Donovan*, K. Okada, X. Zhang, Adjacent Feature for High-Density EMG Pattern Recognition, Submitted to IEEE EMBC, Honolulu, 2018

-        A. Kulkarni, I. Yoon, K. Okada, P. Pennings, C. Domingo, Promoting Diversity in Computing, Submitted to ACM ITiCSE, Lanarca, Cyprus, 2018

-        I. Yoon, K. Okada, A. Kulkarni, P. Pennings, C. Domingo, Promoting Inclusivity in Computing (PINC) via Computing Application Minor, Accepted the American Society for Engineering Education 2018 (ASEE), 2018



-        I. Donovan*, J. Puchin*, K. Okada, X. Zhang, Simple Space-Domain Features for Low-Resolution sEMG Pattern Recognition, In proc. IEEE Eng. Med. Biol. Soc. (EMBC), Korea, 2017 PDF

-        J. Yan*, J. Dalton*, B. Doronila*, K. Chang-Kam*, V. Melara*, C. Thomas*, I. Donovan**, K. Bholla**, A. G. Enriquez, W. Pong, Z. Jiang, C. Chen, K.-S. Teh, H. Mahmoodi, H. Jiang, K. Okada, and X. Zhang, Engaging Community College Students in Computer Engineering Research through Design and Implementation of a Versatile Gesture Control Interface, In proc. the American Society for Engineering Education 2017 Pacific Southwest Conference (ASEE/PSW-2017), Tempe, AZ, April 20-22, 2017. PDF



-        I. Donovan*, K. Valenzuela*, A. Ortiz*, S. Dusheko*, H. Jing, K. Okada, X. Zhang, MyoHMI: A Low-Cost and Flexible Platform for Developing Real-Time Human Machine Interface for Myoelectric Controlled Applications, In proc. IEEE Int Conf  System, Man and Cybernetics (SMC), 2016 PDF

-        D. Petkovic, M. Sosnick-Perez, K. Okada, R. Todtenhoefer, N. Miglani*, A. Vigil*, Using the Random Forest Classifier to Assess and Predict Student Learning of Software Engineering Teamwork, In proc. Frontiers in Education Conference (FIE), 2016 PDF

-        A. Mansoor, G. F. Perez, K. Pancham, A. Jain, K. Okada, M.G. Linguraru, G. R. Nino, Automated Lung Segmentation and Longitudinal Air Trapping Quantification in Chronic Lung Disease of Prematurity, In proc. American Thoracic Society International Conference, pp A4575, 2016 LINK

-        M.G. Linguraru, Q. Zhao, K. Rosenbaum, M. Summar, K. Okada, Device and Method for Classifying a condition based on Image Analysis, US Patent, US9443132 PDF



-        K. Okada, S. Rysavy*, A. Flores*, MG. Linguraru, Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans, Medical Physics, 42(4): 1653-1665 (2015) LINK

-        J. Huo*, J. Alger, H Kim, M Brown, K. Okada, W. Pope, J. Goldin, Between-Scanner and Between-Visit Variation in Normal White Matter Apparent Diffusion Coefficient Values in the Setting of a Multi-Center Clinical Trial, Clinical Neuroradiology, EPub: March 20 (2015) LINK

-        Q. Zhao*, K. Okada, K. Rosenbaum, M. Summar, MG. Linguraru, Constrained Local Model with Independent Component Analysis with Kernel Density Estimation: Application Down syndrome Detection, In proc. IEEE Int. Symp. Biomed. Imaging (ISBI), 967-970, New York, 2015 PDF

-        A. Mansoor, GF. Perez, K. Pancham, A. Khan, K. Okada, G. Nino, MG. Linguraru, Partitioned Active Shape Model With Weighted Landmarks For Accurate Lung Field Segmentation, In proc. Computer-Assisted Radiology and Surgery (CARS) Extended Abstract, S224-6, Barcelona, 2015 PDF

-        K. Okada, M. Golbaz*, A. Mansoor, GF. Perez, K. Pancham, A. Khan, G. Nino, MG. Linguraru, Severity Quantification of Pediatric Viral Respiratory Illnesses in Chest X-Ray Images, In proc. IEEE Eng. Med. Biol. Soc. (EMBC), Invited Paper, Milan, 2015 PDF



-        Q. Zhao, K. Okada, K. Rosenbaum, L. Kehoe, DJ. Zand, R. Sze, M. Summar, MG. Linguraru, Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA, Medical Image Analysis, 18(5): 699-710 (2014) LINK

-        CS. Mendoza, N. Safdar, K. Okada, E. Myers, GF. Rogers, MG. Linguraru, Personalized assessment of craniosynostosis via statistical shape modeling, Medical Image Analysis, 18(4): 635-46 (2014) LINK

-        EA. Simpson, KV, Jakobsen, DM. Fragaszy, K. Okada, JE. Frick, The development of facial identity discrimination through learned attention, Developmental Psychobiology, 56(5): 1083-1101 (2014) LINK

-        Q. Zhao*, N. Werghi, K. Okada, K. Rosenbaum, M. Summar, MG. Linguraru, Ensemble Learning for the Detection of Facial  Dysmorphology, In proc. IEEE Eng. Med. Biol. Soc. (EMBC), 2014:3633-636, Chicago, 2014 PDF

-        D. Petkovic, M. Sosnick-Perez, , S. Huang, R. Todtenhoefer, K. Okada, S. Arora*, R. Sreenivasen*, L. Flores*, S. Dubey*, SETAP: Software Engineering Teamwork Assessment and Prediction Using Machine Learning, In proc. Frontiers in Education Conference (FIE), 2014 PDF

-        J. Hung*, J. Collins*, M. Weldetsion*, O. Newland*, E. Chiang*, K. Okada, Protein crystallization image classification with elastic net, In proc. SPIE Medical Imaging, San Diego, 2014 PDF

-        K. Okada, L. Flores*, M. Wong, D. Petkovic, Microenvironment-Based Protein Function Analysis by Random Forest, In Proc. International Conference on Pattern Recognition, Stockholm, 2014 PDF



-        J. Huo*, K. Okada, EM. van Rikxoort, HJ Kim, JR Alger, WB Pope, JG Goldin, MS Brown, Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging, Medical Physics, 40(9): 093502 (2013) PDF

-        S. Huang, D. Petkovic, K. Okada, M. Sosnick, S. Zhu, R. Todtenhoefer, Toward objective and quantitative assessment and prediction of team work effectiveness in software engineering courses, ACM SIGSOFT Software Engineering Notes, 38(1): 7-9 (2013) PDF

-        A. El-Baz, GM. Beache, GL. Gimel’farb, K. Suzuki, K. Okada, A. Elnakib, A. Soliman, B. Abdollahi, Computer-aided diagnosis systems for lung cancer: challenges and methodologies, International Journal of Biomedical Imaging, 2013:942353 (2013) PDF

-        A. El-Baz, GM. Beache, GL. Gimel’farb, K. Suzuki, K. Okada, Editorial: Lung Imaging Data Analysis, International Journal of Biomedical Imaging, 2013:618561 (2013) LINK

-        Q. Zhao*, K. Okada, K. Rosenbaum, DJ. Zand, R. Sze, M. Summar, MG. Linguraru, Hierarchical Constrained Local model Using ICA and Its Application to Down Syndrome Detection, In Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention (MICCAI), vol.2, pp. 222-229, Nagoya, 2013 PDF

-        Q. Zhao*, K. Rosenbaum, K. Okada, DJ. Zand, R. Sze, M. Summar, MG. Linguraru, Automated down syndrome detection using facial photographs, In proc. IEEE Eng. Med. Biol. Soc. (EMBC), 2013:3670-3, Osaka, 2013 PDF

-        J. Collins*, K. Okada, Learning metrics for content-based medical image retrieval, In proc. IEEE Eng. Med. Biol. Soc. (EMBC), 2013:3363-6, Osaka, 2013 PDF



-        J. Huo*, M.S. Brown, K. Okada, CADrx for GBM Brain Tumors: Predicting Treatment Responses from Changes in Diffusion-Weighted MRI, In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, IGI-Global, 2012 PDF

-        M. Suzuki*, M.G. Linguraru, K. Okada, Multi-Organ Segmentation with Missing Organs in Abdominal CT Images, Accepted, Int. Conf. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2012 PDF

-        D. Petkovic, K. Okada, M. Sosnick*, A. Iyer*, S. Zhu*, R. Todtenhoefer, S. Huang, A Machine Learning Approach for Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education, Accepted, Frontiers in Education Conference (FIE), 2012 PDF

-        C. Kelly*, K. Okada, Variable Interaction Measures with Random Forest Classifiers, In proc. IEEE Symposium on Biomedical Imaging (ISBI), 2012 PDF

-        S. Sergeev*, MG. Linguraru, K. Okada, Medical image registration using machine learning-based interest point detector, In proc. SPIE Medical Imaging, 2012 PDF

-        J. Huo*, K. Okada, W. Pope, M. Brown, Improving semi-automated segmentation by integrating learning by active sampling , In proc. SPIE Medical Imaging, 2012 PDF

-        J. Collins*, K. Okada, A Comparative Study of Similarity Measures for Content-Based Medical Image Retrieval, In CLEF Online Working Notes, 2012 PDF


-        K. Okada, Anisotropic Scale Selection, Robust Gaussian Fitting, and Pulmonary Nodule Segmentation in Chest CT Scans, In Multi-Modality State-Of-The-Art Medical Image Segmentation and Registration Methodologies, Springer, 2011 PDF

-        K. Okada, Ground-Glass Nodule Characterization in High-Resolution CT Scans, In Computer-Aided Diagnosis of Lung Imaging, Taylor and Francis, LLC, 2011 PDF

-        M. Suzuki*, M.G. Linguraru, R.M. Summers, K. Okada, Analyses of Missing Organs in Abdominal Multi-Organ Segmentation, In proc. MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging, 2011 PDF

-        N. Timilsina*, C. Moffatt, K. Okada, Development of a Stained Cell Nuclei Counting System, In proc. SPIE Medical Imaging, 2011 PDF

-        J. Huo*, K. Okada, W. Pope, M. Brown, Sampling-Based Ensemble Segmentation against Inter-Operator variability, In proc. SPIE Medical Imaging, 2011 PDF

-        J. Huo*, E. van Rikxoort, K. Okada, H. Kim, W. Pope, J. Goldin, M. Brown, Confidence-based Ensemble for GBM brain tumor segmentation, In proc. SPIE Medical Imaging 2011 PDF



-        K. Okada, A. Flores*, M. G. Linguraru, Boosting Weighted Linear Discriminant Analysis, International Journal of Advanced Statistics for Economics and Life Sciences, 2(1): 1-11 (2010)

-        K. Okada, Pose-Invariant Face recognition: Analysis, Synthesis, Identification of Human Faces with Pose Variations, VDM Verlag Dr. Mueller, 2010

-        K. Okada, Mean Shift from Theory to Application, In Computer Vision Saisentan Guide 2, Adcom Media, 2010 PDF

-        Y. Miao*, M. Kafai*, K. Okada, Bayesian Classification of Local 3D Structures in Medical Images, In proc. International Workshop on Machine Learning in Medical Imaging (MLMI), 2010 PDF

-        A. Flores*, M. G. Linguraru, K. Okada, Boosted-LDA for Biomedical Data Analysis, In proc. International Workshop on Machine Learning in Medical Imaging (MLMI), 2010 PDF

-        M. Kafai*, Y. Miao*, K. Okada, Directional Mean Shift and its Application for Topology Classification of Local 3D Structures, In proc. IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), 2010 PDF

-        S. Courey, X. Shao*, N. Lam-Miller*, K. Okada, Web-Based Tools for Enhancing Teacher Preparation: Helping to Build a High Quality Teaching Workforce, In proc. World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA), 2010 PDF

-        X. Shao*, N. Lam-Miller*, S. Courey, K. Okada, A Web-based Lesson Plan Creator for Teaching Preparation Programs, In proc.  Annual Conference for Society for Information Technology & Teacher Education (SITE), 2010 PDF



-        J. Huo*, K. Okada, H. Kim, W. Pope, J. Goldin, J. Alger, M. Brown, CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI, Algorithms, 2: 1350 – 1367 (2009) PDF

-        A. Flores*, S. Rysavy*, R. Enciso, K. Okada, Non-Invasive Differential Diagnosis of Dental Periapical Lesions in Cone-Beam CT, In proc. IEEE International Symposium of Biomedical Imaging: From Nano to Macro (ISBI), 2009 PDF

-        K. Okada, N. Lam-Miller*, X. Shao*, S. Courey, Web-based Tools for Credential Candidates in Special Education Programs, In proc. International Conference on Computer Supported Education (CSEDU), 2009 PDF

-        J. Huo*, W. Pope, G. Kim, K. Okada, M. Brown, J. Alger, Y. Wang, J. Goldin, Histogram-based classification with Gaussian Mixture Modeling for GBM Tumor Treatment Response using ADC Map, In proc. SPIE Medical Imaging Conferences, Orland, 2009 PDF



-        K. Okada and C. von der Malsburg, Face Recognition and Pose Estimation with Parametric Linear Subspaces, In Applied Pattern Recognition, H. Bunke, A. Kandel, M. Last (Eds.), pp. 49-74, Springer, 2008 PDF

-        S. Rysavy*, A. Flores*, R. Enciso, K. Okada, Segmentation of Large Periapical Lesions toward Dental Computer-Aided Diagnosis in Cone-Beam CT Scans, In proc. SPIE Medical Imaging Conferences, vol. 6914, pp. 153, San Diego, 2008 [Best poster honorable mention award] PDF

-        S. Rysavy*, A. Flores*, R. Enciso, K. Okada, Classifiability Criteria for Refining of Random Walks Segmentation, In proc. International Conference on Pattern Recognition (ICPR), Tampa, 2008 PDF

-        J. Huo*, W. Pope, K. Okada, J. Alger, HJ. Kim, Y. Wang, J. Goldin, M. Brown, Early Detection of Treatment Response for GBM Brain Tumor using ADC Map of DW-MRI, In proc. MICCAI 2008 Workshop on Computational Diffusion MRI, New York, 2008 PDF

-        K. Okada, S. Periaswamy, J. Bi, Stratified Regularity Measures with Jensen-Shannon Divergence, In proc. IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), Anchorage, 2008 PDF

-        T. Goh*, R. West*, K. Okada, Robust Detection of Semantically Equivalent Visually Dissimilar Objects, In proc. International Workshop on Semantic Learning Applications in Multimedia (SLAM), Anchorage, 2008 PDF



-        H. Ling* and K. Okada, An Efficient Earth Mover’s Distance Algorithm for Robust Histogram Comparison, IEEE Trans. Pattern Analysis and Machine Intelligence, 29(5): 840-863 (2007) PDF

-        J. Martin-Malivel and K. Okada, Human and Chimpanzee Face Recognition in Chimpanzees (Pan troglodytes):  Role of Exposure and Impact on Categorical Perception, Behavioral Neuroscience, 121(6): 1145-1154 (2007) PDF

-        K. Okada and X. Huang, Robust Click-Point Linking: Matching Visually Dissimilar Local Regions, In proc. IEEE International Workshop on Beyond Multiview Geometry: Robust Estimation and Organization of Shapes form Multiple Cue, Minneapolis, 2007 PDF



-        K. Okada, X. Huang, X. Zhou, A. Krishnan, Robust Click-Point Linking for Longitudinal Follow-Up Studies, In proc. Int. Workshop on Medical Imaging and Augmented Reality (MIAR), pp. 252-260, Shanghai, 2006 PDF

-        K. Okada, M. Singh, V. Ramesh, A. Krishnan, Prior-Constrained Scale-Space Mean Shift, In proc. British Machine Vision Conference (BMVC), vol. II, pp. 829-838, Edinburgh, 2006 PDF

-        J. Bi, S. Periaswamy, K. Okada, T. Kubota, G. Fung, M. Salganicoff, B. Rao, Computer Aided Detection via Asymmetric Cascade of Sparse Hyperplane Classifiers, In proc. Annual SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 837-844, Philadelphia, 2006 PDF

-        H. Ling* and K. Okada, Diffusion Distance for Histogram Comparison, In proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), vol. I, pp. 246-253, New York, 2006 PDF

-        H. Ling* and K. Okada, EMD_L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors, In proc. European Conf. Computer Vision (ECCV), vol. III, pp. 330-343, Graz, 2006 PDF

-        C. Bahlmann, X. Li*, K. Okada, Local Pulmonary Structure Classification for Computer-aided Nodule Detection, In proc. SPIE Medical Imaging Conferences, vol. 6144, pp. 1775-1785, San Diego, 2006 PDF



Kazunori Okada, Ph.D.

kazokada AT

Computer Science Department

San Francisco State University

1600 Holloway Ave. San Francisco, CA 94132


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© Kazunori Okada, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

San Francisco State University, San Francisco CA, USA