A TASOM-based algorithm for active contour modeling

Shah‐Hosseini, Hamed; Safabakhsh, Reza

doi:10.1016/s0167-8655(02)00377-x

Cited by 39 publications

(38 citation statements)

References 11 publications

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“…A TASOM (Time Adaptive Self-Organizing Map)-based active contour method detailed in [27], [28] was used to get the inner boundary of the sclera. In [29], the authors have designed enhancement and registration methods to process and match conjunctival vasculature obtained under non-ideal conditions.…”

Section: Fig2 Typical Sclera Biometric Systemmentioning

confidence: 99%

Survey Paper on Sclera Segmentations Techniques

Rajole¹,

Shinde²

2016

International Journal of Advanced Research in Computer and Comm

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Due to extensive growth of internet, security of the internet is area of interest for the researchers. An efficient biometric trait these days are sclera blood vessels. Sclera is a white region in the eye, around the eyeball which contains blood vessel patterns that can be used for personal identification. In this paper we present the existing sclera segmentation methods and their working in with their limitations and ideas to enhance them.

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Section: Fig2 Typical Sclera Biometric Systemmentioning

confidence: 99%

Survey Paper on Sclera Segmentations Techniques

Rajole¹,

Shinde²

2016

International Journal of Advanced Research in Computer and Comm

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“…Snakes can be implemented using an implicit finite difference or through dynamical programming or using methods of finite elements. The first two methods are more common in the literature [7] [8]. Some applications of object segmentation using ACM are: snakes, balloons, template deformation, and dynamical contours.…”

Section: A Definitionmentioning

confidence: 99%

“…Also, the technique is semi-automatic since it needs an initial contour. However, it is useful in various different types of problems such as border extraction, medical images and digital libraries [1] [2][3] [7] [8].…”

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confidence: 99%

Analysis of mammogram using self-organizing neural networks based on spatial isomorphism

Ferreira

Nascimento

Tsang

et al. 2007

2007 International Joint Conference on Neural Networks

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Abstract-The correct segmentation and measurement of mammography images is of fundamental importance for the development of automatic or computer-aided cancer detection systems. In this paper we propose a method to segment mammogram image using a self-organizing neural network based on spatial isomorphism. The method used is a modified version of the algorithm proposed by Venkatesh and Rishikesh [1] to extract object boundaries in an image. This model explores the principle of spatial isomorphism and selforganization in order to create flexible contours that characterize shapes in images. We modified the original algorithm to overcame problems of local minimum, poor performance for image object with large concavity and imprecise results when simple or far from object border contour are chosen. A comparison of both algorithm and original segmentation used by the MIAS database [9] is presented.

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“…An alternative approach to the (above-described parametric) ACMs is a two-layered neural network, based on a self-organizing map (SOM) [13]- [15]. In [13], the authors describe a class of constrained algorithms for object boundary extraction that includes deformable models.…”

Section: A Som-based Network For Contour Extractionmentioning

confidence: 99%

Multiple contour extraction from graylevel images using an artificial neural network

Venkatesh

Raja

Narasimha

2006

IEEE Trans. on Image Process.

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Abstract-For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM-and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of an edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

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A TASOM-based algorithm for active contour modeling

Cited by 39 publications

References 11 publications

Survey Paper on Sclera Segmentations Techniques

Survey Paper on Sclera Segmentations Techniques

Analysis of mammogram using self-organizing neural networks based on spatial isomorphism

Multiple contour extraction from graylevel images using an artificial neural network

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