Statistical approach to X-ray CT imaging and its applications in image analysis. II. A new stochastic model-based image segmentation technique for X-ray CT image

Lei, Tianhu; Sewchand, Wilfred

doi:10.1109/42.126911

Cited by 86 publications

(48 citation statements)

References 9 publications

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“…Although these simulated data may not represent all the characteristics of real images, they provide an accurate ground truth for a first validation of segmentation algorithms. Note that at least for computed tomography images, the noise was found to be Gaussian [48]. Figure 19 shows also that among the three values of σ = 5, 6, 7 for the GVP model, σ = 5 allows the model to be more robust to noise.…”

Section: Experiments On 3d Datamentioning

confidence: 77%

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Rouchdy

Cohen

2013

Computer Vision and Image Understanding

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This paper presents new methods to segment thin tree structures, which are, for example present in microglia extensions and cardiac or neuronal blood vessels. Many authors have used minimal cost paths, or geodesics relative to a local weighting potential P, to find a vessel pathway between two end points.We utilize a set of such geodesic paths to find a tubular tree structure by seeking minimal interaction. We introduce a new idea that we call Geodesic Voting or Geodesic Density. The approach consists of computing geodesics from a set of end points scattered in the image which flow toward a given source point. The target structure corresponds to image points with a high geodesic density. The "Geodesic density" is defined at each pixel of the image as the number of geodesics that pass over this pixel. The potential P is defined in such way that it takes low values along the tree structure, therefore geodesics will migrate toward this structure thereby yielding a high geodesic density. We further adapt these methods to segment complex tree structures in a noisy medium and apply them to segment microglia extensions from confocal microscope images as well as vessels.

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Section: Experiments On 3d Datamentioning

confidence: 77%

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Rouchdy

Cohen

2013

Computer Vision and Image Understanding

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“…This approach is a technique for partitioning an image into distinctive meaningful regions based on the statistical properties of both gray-level and labeled images. Recently, this segmentation technique has received considerable attention for medical image pattern segmentation [13], [14]. However, a good segmentation result would depend on the suitable model selection for a specific image modality [15].…”

Section: Introductionmentioning

confidence: 99%

Morphological filtering and stochastic modeling-based segmentation of masses on mammographic images

Liu

Wang

et al. 1996

1996 IEEE Nuclear Science Symposium. Conference Record

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The objective of this study is to develop an efficient method to highlight the geometric characteristics of mass patterns, and isolate the suspicious regions which in turn provide the improved segmentation of suspected masses. In this work, a combined method of using morphological operations, finite generalized Gaussian mixture modeling, and contextual Bayesian relaxation labeling was developed to enhance and segment various mammographic contexts and textures. This method was applied to segment suspicious masses on mammographic images. The testing results showed that the proposed method can detect all suspected masses as well as high contrast objects and can be used as an effective pre-processing step of mass detection in computer-aided diagnosis systems.

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“…Clusters are then formed in the multidimensional feature vectors. K-means Clustering Methods [24] implement the hard segmentation for a certain number of K clusters while Fuzzy C-means [35] algorithms produce soft segmentations. It works by assigning the membership to pixels of the corresponding cluster in which they have maximum membership coefficients.…”

Section: Clustering and Classificationmentioning

confidence: 99%

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

Zhang

Chang

Yang

et al. 2017

Next Generation Computer Animation Techniques

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Abstract. For surgeons, the precise anatomy structure and its dynamics are important in the surgery interaction, which is critical for generating the immersive experience in VR based surgical training applications. Presently, a normal therapeutic scheme might not be able to be straightforwardly applied to a specific patient, because the diagnostic results are based on averages, which result in a rough solution. Patient Specific Modeling (PSM), using patient-specific medical image data (e.g. CT, MRI, or Ultrasound), could deliver a computational anatomical model. It provides the potential for surgeons to practice the operation procedures for a particular patient, which will improve the accuracy of diagnosis and treatment, thus enhance the prophetic ability of VR simulation framework and raise the patient care. This paper presents a general review based on existing literature of patient specific surgical simulation on data acquisition, medical image segmentation, computational mesh generation, and soft tissue real time simulation.

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Statistical approach to X-ray CT imaging and its applications in image analysis. II. A new stochastic model-based image segmentation technique for X-ray CT image

Cited by 86 publications

References 9 publications

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Morphological filtering and stochastic modeling-based segmentation of masses on mammographic images

Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution

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