Adaptive Re-Segmentation Strategies for Accurate Bright Field Cell Tracking

Understanding complex interactions in cellular systems requires accurate tracking of individual cells observed in microscopic image sequence and acquired from multi-day in vitro experiments. To be effective, methods must follow each cell through the whole experimental sequence to recognize significant phenotypic transitions, such as mitosis, chemotaxis, apoptosis, and cell/cell interactions, and to detect the effect of cell treatments. However, high accuracy long-range cell tracking is difficult because the collection and detection of cells in images is error-prone, and single error in a one frame can cause a tracked cell to be lost. Detection of cells is especially difficult when using bright field microscopy images wherein the contrast difference between the cells and the background is very low. This work introduces a new method that automatically identifies and then corrects tracking errors using a combination of combinatorial registration, flow constraints, and image segmentation repair

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

Segmentation of striatal brain structures from high resolution pet images

Dissertation presented at the Faculty of Science and Technology of the New University of Lisbon in fulfillment of the requirements for the Masters degree in Electrical Engineering and ComputersWe propose and evaluate fully automatic segmentation methods for the extraction of striatal brain surfaces (caudate, putamen, ventral striatum and white matter), from high resolution positron emission tomography (PET) images. In the preprocessing steps, both the right and the left striata were segmented from the high resolution PET images. This segmentation was achieved by delineating the brain surface, finding the plane that maximizes the reflective symmetry of the brain (mid-sagittal plane) and, finally, extracting the right and left striata from both hemisphere images. The delineation of the brain surface and the extraction of the striata were achieved using the DSM-OS (Surface Minimization – Outer Surface) algorithm. The segmentation of striatal brain surfaces from the striatal images can be separated into two sub-processes: the construction of a graph (named “voxel affinity matrix”) and the graph clustering. The voxel affinity matrix was built using a set of image features that accurately informs the clustering method on the relationship between image voxels. The features defining the similarity of pairwise voxels were spatial connectivity, intensity values, and Euclidean distances. The clustering process is treated as a graph partition problem using two methods, a spectral (multiway normalized cuts) and a non-spectral (weighted kernel k-means). The normalized cuts algorithm relies on the computation of the graph eigenvalues to partition the graph into connected regions. However, this method fails when applied to high resolution PET images due to the high computational requirements arising from the image size. On the other hand, the weighted kernel k-means classifies iteratively, with the aid of the image features, a given data set into a predefined number of clusters. The weighted kernel k-means and the normalized cuts algorithm are mathematically similar. After finding the optimal initial parameters for the weighted kernel k-means for this type of images, no further tuning is necessary for subsequent images. Our results showed that the putamen and ventral striatum were accurately segmented, while the caudate and white matter appeared to be merged in the same cluster. The putamen was divided in anterior and posterior areas. All the experiments resulted in the same type of segmentation, validating the reproducibility of our results

Immunohistochemistry image analysis : protein, nuclei and gland

This thesis focus on the analysis of digitized microscopic image, especially on IHC stained colour images. The corresponding contributions focused on the automatic detection of stain colour and glands, the segmentation and quantification of cell nuclei, the analysis of liver cirrhosis and the development of a semi-automatic toolbox. Colour is the most important feature in the analysis of immunostained images. We developed a statistical colour detection model for stain colour detection based on the histograms of collected colour pixels. This is acting on the approach "what you see is what you get" which outperforms the other methods on the detection of several kinds of stain colour. Verifying the presence of nuclei and quantifying positive nuclei is the foundation of cancer grading. We developed a novel seeded nuclei segmentation method which greatly improves the segmentation accuracy and reduces both over-segmentation and under-segmentation. This method has been demonstrated to be robust and accurate in both segmentation and quantification against manual labelling and counting in the evaluation process. The analysis of gland architecture, which reflects the cancer stage, has evolved into an important aspect of cancer detection. A novel morphology-based approach has been developed to segment gland structures in H-DAB stained images. This method locates the gland by focusing on its morphology and intensity characteristics, which covers variations in stain colours in different IHC images. The evaluation results have demonstrated the improvements of accuracy and efficiency. For the successive development of three methods, we put them in a semi-automatic toolbox for the aid of IHC image analysis. It can detect different kinds of stain colour and the basic components in an IHC image. The user created models and parameters can be saved and transferred to different users for the reproduction of detection results in different laboratories. To demonstrate the flexibility of our developed stained colour detection technique, the tool has been extended to the analysis of liver cirrhosis. It is a novel method based on our statistical colour detection model which greatly improves the analysis accuracy and reduces the time cost

Perceptual watersheds for cell segmentation in fluorescence microscopy images

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.High content screening aims to analyze complex biological systems and collect quantitative data via automated microscopy imaging to improve the quality of molecular cellular biology research in means of speed and accuracy. More rapid and accurate high-throughput screening becomes possible with advances in automated microscopy image analysis, for which cell segmentation commonly constitutes the core step. Since the performance of cell segmentation directly a ects the output of the system, it is of great importance to develop e ective segmentation algorithms. Although there exist several promising methods for segmenting monolayer isolated and less con uent cells, it still remains an open problem to segment more con uent cells that grow in aggregates on layers. In order to address this problem, we propose a new marker-controlled watershed algorithm that incorporates human perception into segmentation. This incorporation is in the form of how a human locates a cell by identifying its correct boundaries and piecing these boundaries together to form the cell. For this purpose, our proposed watershed algorithm de nes four di erent types of primitives to represent di erent types of boundaries (left, right, top, and bottom) and constructs an attributed relational graph on these primitives to represent their spatial relations. Then, it reduces the marker identi cation problem to the problem of nding prede ned structural patterns in the constructed graph. Moreover, it makes use of the boundary primitives to guide the ooding process in the watershed algorithm. Working with uorescence microscopy images, our experiments demonstrate that the proposed algorithm results in locating better markers and obtaining better cell boundaries for both less and more con uent cells, compared to previous cell segmentation algorithms.Arslan, SalimM.S

Immunohistochemistry image analysis : protein, nuclei and gland

This thesis focus on the analysis of digitized microscopic image, especially on IHC stained colour images. The corresponding contributions focused on the automatic detection of stain colour and glands, the segmentation and quantification of cell nuclei, the analysis of liver cirrhosis and the development of a semi-automatic toolbox. Colour is the most important feature in the analysis of immunostained images. We developed a statistical colour detection model for stain colour detection based on the histograms of collected colour pixels. This is acting on the approach "what you see is what you get" which outperforms the other methods on the detection of several kinds of stain colour. Verifying the presence of nuclei and quantifying positive nuclei is the foundation of cancer grading. We developed a novel seeded nuclei segmentation method which greatly improves the segmentation accuracy and reduces both over-segmentation and under-segmentation. This method has been demonstrated to be robust and accurate in both segmentation and quantification against manual labelling and counting in the evaluation process. The analysis of gland architecture, which reflects the cancer stage, has evolved into an important aspect of cancer detection. A novel morphology-based approach has been developed to segment gland structures in H-DAB stained images. This method locates the gland by focusing on its morphology and intensity characteristics, which covers variations in stain colours in different IHC images. The evaluation results have demonstrated the improvements of accuracy and efficiency. For the successive development of three methods, we put them in a semi-automatic toolbox for the aid of IHC image analysis. It can detect different kinds of stain colour and the basic components in an IHC image. The user created models and parameters can be saved and transferred to different users for the reproduction of detection results in different laboratories. To demonstrate the flexibility of our developed stained colour detection technique, the tool has been extended to the analysis of liver cirrhosis. It is a novel method based on our statistical colour detection model which greatly improves the analysis accuracy and reduces the time cost

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon