Filiz Bunyak
University of Missouri
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Publication
Featured researches published by Filiz Bunyak.
international conference on information fusion | 2010
Kannappan Palaniappan; Filiz Bunyak; Praveen Kumar; Ilker Ersoy; Stefan Jaeger; Koyeli Ganguli; Anoop Haridas; Joshua Fraser; Raghuveer M. Rao
Very large format video or wide-area motion imagery (WAMI) acquired by an airborne camera sensor array is characterized by persistent observation over a large field-of-view with high spatial resolution but low frame rates (i.e. one to ten frames per second). Current WAMI sensors have sufficient coverage and resolution to track vehicles for many hours using just a single airborne platform. We have developed an interactive low frame rate tracking system based on a derived rich set of features for vehicle detection using appearance modeling combined with saliency estimation and motion prediction. Instead of applying subspace methods to very high-dimensional feature vectors, we tested the performance of feature fusion to locate the target of interest within the prediction window. Preliminary results show that fusing the feature likelihood maps improves detection but fusing feature maps combined with saliency information actually degrades performance.
computer vision and pattern recognition | 2014
Rui Wang; Filiz Bunyak; Kannappan Palaniappan
In this paper, we present a moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-of-the-art methods.
medical image computing and computer assisted intervention | 2006
Sumit Kumar Nath; Kannappan Palaniappan; Filiz Bunyak
Current level-set based approaches for segmenting a large number of objects are computationally expensive since they require a unique level set per object (the N-level set paradigm), or [log2N] level sets when using a multiphase interface tracking formulation. Incorporating energy-based coupling constraints to control the topological interactions between level sets further increases the computational cost to O(N2). We propose a new approach, with dramatic computational savings, that requires only four, or fewer, level sets for an arbitrary number of similar objects (like cells) using the Delaunay graph to capture spatial relationships. Even more significantly, the coupling constraints (energy-based and topological) are incorporated using just constant O(1) complexity. The explicit topological coupling constraint, based on predicting contour collisions between adjacent level sets, is developed to further prevent false merging or absorption of neighboring cells, and also reduce fragmentation during level set evolution. The proposed four-color level set algorithm is used to efficiently and accurately segment hundreds of individual epithelial cells within a moving monolayer sheet from time-lapse images of in vitro wound healing without any false merging of cells.
international symposium on biomedical imaging | 2006
Filiz Bunyak; Kannappan Palaniappan; Sumit Kumar Nath; Tobias I. Baskin; Gang Dong
Quantifying the behavior of cells individually, and in clusters as part of a population, under a range of experimental conditions, is a challenging computational task with many biological applications. We propose a versatile algorithm for segmentation and tracking of multiple motile epithelial cells during wound healing using time-lapse video. The segmentation part of the proposed method relies on a level set-based active contour algorithm that robustly handles a large number of cells. The tracking part relies on a detection-based multiple-object tracking method with delayed decision enabled by multi-hypothesis testing. The combined method is robust to complex cell behavior including division and apoptosis, and to imaging artifacts such as illumination changes
advanced concepts for intelligent vision systems | 2008
Adel Hafiane; Filiz Bunyak; Kannappan Palaniappan
Histopathology imaging provides high resolution multispectral images for study and diagnosis of various types of cancers. The automatic analysis of these images can greatly facilitate the diagnosis task for pathologists. A primary step in computational histology is accurate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other guiding structures such as lumen and epithelial regions which together make up a gland structure. This paper presents a new method for gland structure segmentation and nuclei detection. In the first step, fuzzy c-means with spatial constraint algorithm is applied to detect the potential regions of interest, multiphase vector-based level set algorithm is then used to refine the segmentation. Finally, individual nucleus centers are detected from segmented nuclei clusters using iterative voting algorithm. The obtained results show high performances for nuclei detection compared to the human annotation.
international conference on image processing | 2008
Ilker Ersoy; Filiz Bunyak; Michael A. Mackey; Kannappan Palaniappan
The large amount of data produced by biological live cell imaging studies of cell behavior requires accurate automated cell segmentation algorithms for rapid, unbiased and reproducible scientific analysis. This paper presents a new approach to obtain precise boundaries of cells with complex shapes using ridge measures for initial detection and a modified geodesic active contour for curve evolution that exploits the halo effect present in phase-contrast microscopy. The level set contour evolution is controlled by a novel spatially adaptive stopping function based on the intensity profile perpendicular to the evolving front. The proposed approach is tested on human cancer cell images from LSDCAS and achieves high accuracy even in complex environments.
Advances in Experimental Medicine and Biology | 2011
Filiz Bunyak; Adel Hafiane; Kannappan Palaniappan
High resolution, multispectral, and multimodal imagery of tissue biopsies is an indispensable source of information for diagnosis and prognosis of diseases. Automatic extraction of relevant features from these imagery is a valuable assistance for medical experts. A primary step in computational histology is accurate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other structures such as lumen and epithelial regions which together make up a gland structure. This chapter presents an automatic segmentation system for histopathology imaging. Spatial constraint fuzzy C-means provides an unsupervised initialization. An active contour algorithm that combines multispectral edge and region informations through a vector multiphase level set framework and Beltrami color metric tensors refines the segmentation. An improved iterative kernel filtering approach detects individual nuclei centers and decomposes densely clustered nuclei structures. The obtained results show high performances for nuclei detection compared to the human annotation.
medical image computing and computer assisted intervention | 2009
Ilker Ersoy; Filiz Bunyak; Vadim Chagin; M. Christina Cardoso; Kannappan Palaniappan
Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.
The Journal of Physiology | 2014
Zhongkui Hong; Zhe Sun; Min Li; Zhaohui Li; Filiz Bunyak; Ilker Ersoy; Jerome P. Trzeciakowski; Marius C. Staiculescu; Minshan Jin; Luis A. Martinez-Lemus; Michael A. Hill; Kannappan Palaniappan; Gerald A. Meininger
This study demonstrates rapid and dynamic changes in adhesion and cell elasticity following agonist stimulation that culminate in a remodelled cytoskeleton in vascular smooth muscle. Evidence is presented that the changes in adhesion and elasticity are coordinated and that these variables demonstrate temporal oscillation consisting of three major oscillation components. Eigen‐decomposition spectrum analysis revealed that these components of oscillation in cell elasticity and adhesion may be linked by shared signalling pathways. Evidence is provided that the agonists angiotensin II and adenosine produce remodelling of actin cytoskeleton that may alter the properties of the observed oscillations in elasticity and adhesion. It is concluded that angiotensin II and adenosine may regulate extracellular matrix adhesion and elasticity in vascular smooth muscle cells as a form of adaptation to more efficiently support contractile behaviour.
international conference on pattern recognition | 2008
Adel Hafiane; Filiz Bunyak; Kannappan Palaniappan
Computer assisted or automated histological grading of tissue biopsies for clinical cancer care is a long-studied but challenging problem. It requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for quantitative pathology of cancer tissues. We describe a novel approach for tissue segmentation using fuzzy spatial clustering, vector-based multiphase level set active contours and nuclei detection using an iterative kernel voting scheme that is robust even in the case of clumped touching nuclei. Early results show that we can reach a 91% detection rate compared to manual ground truth of cell nuclei centers across a range of prostate cancer grades.