Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods

Maheshan, M. S.; Harish, B. S.; Kumar, Senthil

doi:10.5220/0008935704330439

Cited by 5 publications

(4 citation statements)

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“…To evaluate the effectiveness of the proposed work, experimentation is conducted against the traditional FCM and its variants (which includes Kernel FCM (KFCM), Spatial FCM (SFCM) and Spatial Kernel FCM (SKFCM)). Furthermore, the recent contributions of [32,33] which is an extension of kernel-based methods viz., Robust Spatial Kernel FCM (RSKFCM), Generalized Spatial Kernel FCM (GSKFCM) are also being compared and it is observed from Tables 2 and 3 that the proposed MIFCM has an upper hand than the existing methods. In Fig.…”

Section: Discussionmentioning

confidence: 99%

A Modified Intuitionistic Fuzzy Clustering Approach for Sclera Segmentation

Maheshan

Harish

2021

SN COMPUT. SCI.

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The culmination of technology-driven society has bought individuals in a lot of digital transactions. How legitimate these transactions are, is the question of the hour. Biometric-enabled transactions have gained popularity. Sclera, a new biometricbased recognition system promises to add value to such transactions. However, this recognition is purely based on the effective segmentation of the sclera from the occluded region of the eye. This work proposes a Modified Intuitionistic Fuzzy Clustering approach for the effective segmentation of sclera images. The traditional fuzzy set assumes that the non-membership value is always the complement of the membership value. But in the true sense, this assumption is not always correct because of hesitation. To alleviate the problems of hesitation degree and noise in the images, the Modified Intuitionistic Fuzzy C-Means (MIFCM) is proposed and tested against the Sclera Segmentation and Recognition Benchmarking Competition (SSRBC2016) and Sclera Segmentation Benchmarking Competition (SSBC 2019) dataset. The experimentation results reveal that the proposed work complements the other existing methods and variants of Fuzzy C-Means.

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Section: Discussionmentioning

confidence: 99%

A Modified Intuitionistic Fuzzy Clustering Approach for Sclera Segmentation

Maheshan

Harish

2021

SN COMPUT. SCI.

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“…To overcome this disadvantage quoted above, Spatial Kernel Fuzzy Clustering methods [24] is employed on spots of the microarray images which is a non-Euclidean distance measure with modified approach of using spatial information/constraints. Hybrid kernel function technique [25] also suffers limitation of high computational speed and single parameter input restriction. This can be addressed using Robust Spatial Kernel FCM (RSKFCM) method which primarily lowers the computational speed.…”

Section: Methodsmentioning

confidence: 99%

Spatial Kernel Fuzzy Clustering Methods for Microarray Image Spot Segmentation

Priya,

Roopa,

Harish

et al. 2023

IEEE Access

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Computational Intelligence (CI) techniques are used in Microarray image processing which is useful for gene profiling in the medical field, drug discovery and industrial research. The paper discusses microarray image processing technique specifically spot segmentation using clustering techniques. This interdisciplinary approach helps in analyzing biological sequences and genome content to identify the function of macro molecules. The fast-evolving techniques of CI with Fuzzy Intelligence (FI) paradigms helps in distinguishing gene information, protein expression calculation, molecular information discovery and genetic study from segmented spots of microarray images. The clustering methods proposed in this paper provide support in analyzing characteristics of data point which belong to particular cluster. The limitation of evolutionary Fuzzy C-Means (FCM) method is discussed in the paper. A significant approach is discussed to overcome above said limitations with two variants of FCM being proposed -Robust Spatial Kernel FCM (RSKFCM) and Generalized Spatial Kernel FCM (GSKFCM). The iterative approach converges the spots of microarray image to the aligned membership values of neighborhood which leads to clustering of spots. The discussed method uses Gaussian kernel function which helps in reducing noise impact amicably.

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“…Maheshan et al [4] proposed the segmentation of sclera from the eye image using a modified intuitionistic fuzzy clustering approach. This is aimed at improving the performance of the traditional fuzzy set that non-membership value is always the complement of the membership value.…”

Section: Sclera Segmentationmentioning

confidence: 99%

“…The iris is noted for its accuracy in near infrared images; however, this drops in visible light wavelength with no constraint during capturing [4]. To alleviate the challenges associated with both traits, pairing the trait with another would improve the performance of iris [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

Taiwo Adeniyi,

Tinuke Omolewa,

Kehinde Adeniyi

2024

TELKOMNIKA

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Security challenges over the years has led to the need for an improvement in the traditional security approaches. This led to the advent of biometrics. Recently, among the biometric approaches, sclera has been an area of imense study. This is due to its accuracy; however, segmentation of the sclera has been a limiting factor to the application of this biometric trait. Several approaches have been proposed in literature but there is still the need to improve the segmentation accuracy. This study proposes the use of circular hough transform and a modified run-data based algorithm. The study also presented a sclera recognition system using the compound local binary pattern for features extraction and Manhattan distance for classification. The system produced a segmentation accuracy of 99.9% for sclera blood vessels, periocular and iris (SBVPI) sclera database and 100% for manually captured sclera database. The system produced an accuracy of 99.98 for SBVPI sclera database and 99.99% for manually captured sclera database.

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Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods

Cited by 5 publications

References 0 publications

A Modified Intuitionistic Fuzzy Clustering Approach for Sclera Segmentation

A Modified Intuitionistic Fuzzy Clustering Approach for Sclera Segmentation

Spatial Kernel Fuzzy Clustering Methods for Microarray Image Spot Segmentation

Sclera boundary localization using circular hough transform and a modified run-data based algorithm

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