Using active contours and mathematical morphology tools for quantification of immunohistochemical images

Elmoataz, Abderrahim; Schüpp, Sophie; Clouard, Régis; Herlin, Paulette; Bloyet, Daniel

doi:10.1016/s0165-1684(98)00146-7

Cited by 19 publications

(11 citation statements)

References 19 publications

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“…Unfortunately, none of these methods can handle multi-part objects such as axons surrounded by a myelin sheath. Lately, several authors have relied on active contour models, or snakes (Amini et al, 1990;Fok et al, 1996;Elmoataz et al, 1998), to handle both local and structural analysis in one step. After detecting candidates through a global tool such as the Hough transform, each region of interest is processed individually with an explicit active contour model evolving towards the real contours of the structure.…”

Section: Introductionmentioning

confidence: 99%

Automatic morphometry of nerve histological sections

Romero

Cuisenaire

Denef

et al. 2000

Journal of Neuroscience Methods

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A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.

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Section: Introductionmentioning

confidence: 99%

Automatic morphometry of nerve histological sections

Romero

Cuisenaire

Denef

et al. 2000

Journal of Neuroscience Methods

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“…For such applications active contour-based models or snakes, first introduced in [24], are preferred. They have a wide range of application; see, e.g., [25]- [33].…”

Section: A Snakes: Active Contour-based Modelsmentioning

confidence: 99%

3-D reconstruction of tissue components for atherosclerotic human arteries using ex vivo high-resolution MRI

Auer

Stollberger

Regitnig

et al. 2006

IEEE Trans. Med. Imaging

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Automatic computer-based methods are well suited for the image analysis of the different components in atherosclerotic plaques. Although several groups work on such analysis some of the methods used are oversimplified and require improvements when used within a computational framework for predicting meaningful stress and strain distributions in the heterogeneous arterial wall under various loading conditions. Based on high-resolution magnetic resonance imaging of excised atherosclerotic human arteries and a series of two-dimensional (2-D) contours we present a segmentation tool that permits a three-dimensional (3-D) reconstruction of the most important tissue components of atherosclerotic arteries. The underlying principle of the proposed approach is a model-based snake algorithm for identifying 2-D contours, which uses information about the plaque composition and geometric data of the tissue layers. Validation of the computer-generated tissue boundaries is performed with 100 MR images, which are compared with the results of a manual segmentation performed by four experts. Based on the Hausdorff distance and the average distance for computer-to-expert differences and the interexpert differences for the outer boundary of the adventitia, the adventitia-media, media-intima, intima-lumen and calcification boundaries are less than 1 pixel (0.234 mm). The percentage statistic shows similar results to the modified Williams index in terms of accuracy. Except for the identification of lipid-rich regions the proposed algorithm is automatic. The nonuniform rational B-spline-based computer-generated 3-D models of the individual tissue components provide a basis for clinical and computational analysis.

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“…Further details on seed detection and active contours are beyond the scope of this paper because they are obtained and implemented using standard approaches. The interested readers are directed to [8] and [6].…”

Section: Virtual Cell Membrane Detectionmentioning

confidence: 99%

“…Much previous work in biomedical image processing focused on automated methods for segmentation of nuclei and cells [4][5] [6] [7]. Classical approaches, such as active contours or watersheds, are not effective when the objects to be identified lack specific geometrical features or gradient variations.…”

Section: Introductionmentioning

confidence: 99%