A supervised texture-based active contour model with linear programming

Olivier, Julien; Mocquillon, Cédric; Rousselle, Jean-Jacques; Boné, Romuald; Cardot, Hubert

doi:10.1109/icip.2008.4711952

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Unsupervised methods based AC does not use any prior knowledge. In contrast, supervised method based AC rests on a prior knowledge about the type and number of textures to be segmented [2,6,7,12]. Basically, supervised texture segmentation consists of discriminating texture features to build a partition of the image into homogeneous regions [8,12].…”

Section: Introductionmentioning

confidence: 94%

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Fast globally supervised segmentation by active contours with shape and texture descriptors

Derraz¹,

Thiran

Taleb‐Ahmed

et al. 2012

2012 19th IEEE International Conference on Image Processing

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We present a new globally supervised segmentation method in the characteristic function framework based on an active contours (AC) model incorporating both shape prior and texture descriptors. The shape prior descriptor is formulated as the traditional Legendre moment and the texture descriptor as a linear combination of local inside/outside texture descriptor. Using these two descriptors, the AC energy incorporates both learned textures and training shapes. This formulation has two main advantages: 1) by discriminating independently the foreground/background textures. 2) by incorporating both the learned inside/outside texture and the training shape. The trade-off between inside and outside texture descriptor is ensured by balancing descriptor. We illustrate the performance of our segmentation algorithm using some challenging textured images.

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Section: Introductionmentioning

confidence: 94%

“…The region-based term is usually expressed as a domain integral of the region descriptor / in out T V where homogeneity criterion: (12) In the general case the function / …”

Section: Region Descriptormentioning

confidence: 99%

Fast globally supervised segmentation by active contours with shape and texture descriptors

Derraz¹,

Thiran

Taleb‐Ahmed

et al. 2012

2012 19th IEEE International Conference on Image Processing

View full text Add to dashboard Cite

show abstract

“…The segmentation algorithms can be widely categorized into three types viz, supervised, unsupervised and interactive. Supervised segmentation methods need manually labeled training data for recognizing specific region of interest in images, which may restrict the scope of this method [1][2][3][4]. Unsupervised (automatic) methods provide segmentation results without prior information about the input images and don't require manual intervention [5].…”

Section: Introductionmentioning

confidence: 99%

A voyage on medical image segmentation algorithms

Sn¹,

A²,

Muthukumar³

et al. 2018

biomedicalresearch

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Image segmentation is the process of subdividing an image into eloquent regions that are consistent and homogeneous in some characteristics. Image segmentation is indeed a vital process in the early diagnosis of abnormalities and treatment planning. The segmentation algorithms are employed to extract the anatomical structures and anomalies from medical images. The segmentation algorithms can be categorized into three generations. The first generation algorithms are based on threshold, seed point selection and edge tracing methods. The second generation algorithms incorporate uncertainty and optimization models and the third generation algorithms considers the prior information in segmentation process. This review work discusses and conceptualizes the various segmentation algorithms, which are in correlation with medical images and adduce the result of some of the significant algorithms in each generation. Moreover, the proposed work does spell out the pros and cons of the algorithms for computer aided analysis. In extension, this literature review indeed paves an ample platform to the researchers for better understanding of various segmentation techniques and its characteristics for medical images.

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Prostate Image Segmentation Using Ant Colony Optimization-Boundary Complete Recurrent Neural Network (ACO-BCRNN)

Ramesh

Manavalan

2021

Computer Networks, Big Data and IoT

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A supervised texture-based active contour model with linear programming

Cited by 4 publications

References 12 publications

Fast globally supervised segmentation by active contours with shape and texture descriptors

Fast globally supervised segmentation by active contours with shape and texture descriptors

A voyage on medical image segmentation algorithms

Prostate Image Segmentation Using Ant Colony Optimization-Boundary Complete Recurrent Neural Network (ACO-BCRNN)

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