Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models — ‘snakes’

Maksimović, Ružica; Stanković, Srdjan; Milovanović, Dragan

doi:10.1016/s1386-5056(00)00073-3

Cited by 56 publications

(31 citation statements)

References 11 publications

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“…Common1y used brain segmentation approaches with CT scan are using active contour model [19], thresholding [5], and region growing method [10]. Among them, thresholding 261 is a simple, but powerful solution to separate bleeding areasfrom other components of the brain.…”

Section: IImentioning

confidence: 99%

Designing a robust bleeding detection method for brain CT image analysis

Shirgaonkar

Jeong

Huynh

et al. 2012

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops

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Approximately 7 million people each year in the world suffer from brain injuries caused by motor vehicle accidents, falls, or assaults. Thus, correctly identifying bleeding in the brain is critical to make fast and reliable treatments and diagnostic decisions for proving better cares to brain injury patients. Although it is very challenging to detect bleeding areas in low resolution Computed Tomography (CT) images having complex bleeding patterns, developing an automated detection method can significantly help physicians understand bleeding patterns and determine the severity of brain injuries. In this paper, we propose a fast and robust hybrid method to detect bleeding areas on clinical brain CT images. Specifically, our proposed method follows several steps to segment bleeding areas in brain CT images as eliminating noise, detecting and separating skull regions, applying a combined approach of histogram and a modified global thresholding. By applying our approach to 30 brain CT image, we found the accuracy of 90% in identifying bleeding areas correctly.

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Section: IImentioning

confidence: 99%

Designing a robust bleeding detection method for brain CT image analysis

Shirgaonkar

Jeong

Huynh

et al. 2012

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops

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“…hyperspectral images in remote sensing problems [1], and in 3D computed tomography images [11]. The basis of concern for us here is the set of segmentation membership functions in the two spatial dimensions, which take on values of either 0 or 1 for hard segmentation or within the interval [0, 1] for fuzzy segmentation.…”

Section: Joint Reconstruction and Segmentation Of Spectral Images Takmentioning

confidence: 99%

Reconstructing and segmenting hyperspectral images from compressed measurements

Zhang

Plemmons

Kittle

et al. 2011

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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A joint reconstruction and segmentation model for hyperspectral data obtained from a compressive measurement system is described. Although hyperspectral imaging (HSI) technology has incredible potential, its utility is currently limited because of the enormous quantity and complexity of the data it gathers. Yet, often the scene to be reconstructed from the HSI data contains far less information, typically consisting of spectrally and spatially homogeneous segments that can be represented sparsely in an appropriate basis. Such vast informational redundancy thus implicitly contained in the HSI data warrants a compressed sensing (CS) strategy that acquires appropriately coded spectral-spatial data from which one can reconstruct the original image more efficiently while still enabling target identification procedures. A codedaperture snapshot spectral imager (CASSI) that collects compressed measurements is considered here, and a joint reconstruction and segmentation model for hyperspectral data obtained from CASSI compressive measurements is described. Promising test results on simulated and real data are reported.

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“…Deformable model methods include active contours or snakes, level sets and wave-front propagation. 1,8,9,17,19 These methods work on iteratively adjusting the initial user set contour to fit to the blood vessel structure. The major disadvantages of these methods are that there is little use of structure information leading to high chance of leakage, and also that iteratively modifying the contour is very time consuming.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis

et al. 2014

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The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

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Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models — ‘snakes’

Cited by 56 publications

References 11 publications

Designing a robust bleeding detection method for brain CT image analysis

Designing a robust bleeding detection method for brain CT image analysis

Reconstructing and segmenting hyperspectral images from compressed measurements

Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis

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