Zulaikha Beevi - Academia.edu (original) (raw)

Papers by Zulaikha Beevi

Research paper thumbnail of Namib Beetle Firefly Optimization enabled Densenet architecture for hyperspectral image segmentation and classification

International Journal of Image and Data Fusion, Jan 12, 2024

Research paper thumbnail of A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. ... more Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed clustering algorithm uses fuzzy spatial information to calculate membership value. The input image is clustered using proposed ISFCM algorithm. A comparative study has been made between the conventional FCM and proposed ISFCM. The proposed approach is found to be outperforming the conventional FCM.

Research paper thumbnail of Study of medical image segmentation algorithms

Research paper thumbnail of Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning

Biomedical Signal Processing and Control, Jul 1, 2023

Research paper thumbnail of Decision Making Algorithm for Blind Navigation Assistance using Deep Learning

2022 1st International Conference on Computational Science and Technology (ICCST), Nov 9, 2022

Research paper thumbnail of A Novel Method for Character Segmentation of Vehicle License Plate

International journal of research in engineering and technology, Nov 25, 2013

Segmentation is a part of License Plate Recognition (LPR) technique, used to find out a vehicle b... more Segmentation is a part of License Plate Recognition (LPR) technique, used to find out a vehicle by its number plate without direct human involvement. Segmentation is a process of partitioning a digital image into multiple segments. The objective of segmentation is to simplify and/or change the representation of an image into something that is more expressive and easier to analyze. The proposed algorithm focuses on segmenting the characters of two rows license plate image. Before the segmentation algorithm is applied, the License plate must be localized correctly by using localization algorithm.

Research paper thumbnail of Robust segmentation algorithm using LOG edge detector for effective border detection of noisy skin lesions

... et al. developed a segmentation algorithm where the the entropy is used as stopping criterion... more ... et al. developed a segmentation algorithm where the the entropy is used as stopping criterion in the segmentation process by using recursively the mean shift filtering [8]. Teresa Mendonca et al. performed the segmentation ...

Research paper thumbnail of Optimal Routing Protocol for Wireless Sensor Network Using Genetic Fuzzy Logic System

Computers, materials & continua, 2022

The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it m... more The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data delivery is also considered to be an effective approach to save energy. To achieve this objective, we propose a new energy efficient routing protocol using genetic fuzzy logic system. Our primary objective is to save energy by sending data packets via the shortest path. Numerous studies have proved that the clustering protocol plays an important role in prolonging the life of the sensor node in the WSN. Keeping up with this our second objective is selection of head node from a cluster. This cluster head is selected based on the availability of maximum residual energy among the nodes, lifetime of head-to-head link, and its minimum distance to the base station. The genetic fitness approach is proposed for optimal routing and the selection of cluster head (CH) is employed with fuzzy logic system. As a result, the genetic fuzzy logic system (GFLS) can effectively accelerate the process to solve this problem. MATLAB is used to deploy nodes in WSN. The performance is calculated in terms of efficiency, delay, packet delivery rate and network throughput. The performance is compared with previous pertinent work. The proposed approach has elevated its performance around 8% in packet delivery and 6% in overall network throughput.

Research paper thumbnail of A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation

arXiv (Cornell University), Apr 10, 2010

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. ... more Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed clustering algorithm uses fuzzy spatial information to calculate membership value. The input image is clustered using proposed ISFCM algorithm. A comparative study has been made between the conventional FCM and proposed ISFCM. The proposed approach is found to be outperforming the conventional FCM.

Research paper thumbnail of A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability

The International Arab Journal of Information Technology, 2012

Image segmentation plays a major role in medical imaging applications. During last decades, devel... more Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is highly sensitive to noise because it segments images on the basis of intensity values. In this paper, for the segmentation of noisy medical images, an effective approach is presented. The proposed approach utilizes histogram based Fuzzy C-Means clustering algorithm for the segmentation of medical images. To improve the robustness against noise, the spatial probability of the neighboring pixels is integrated in the objective function of FCM. The noisy medical images are denoised, with the help of an effective denoising algorithm, prior to segmentation, to increase further the approach's robustness. A comparative analysis is done between the conventional FCM and the proposed approach. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy despite of noise levels. From the experimental results, it is also clear that the proposed approach is more efficient and robust against noise when compared to that of the FCM.

Research paper thumbnail of Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application

Communications in computer and information science, 2022

Research paper thumbnail of A Hybrid Region Growing Algorithm for Medical Image Segmentation

International Journal of Computer Science and Information Technology, Jun 30, 2012

In this paper, we have made improvements in region growing image segmentation. The First one is s... more In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Research paper thumbnail of Hybrid Segmentation Approach using FCM and Dominant Intensity Grouping with Region Growing on Medical Image

International Journal of Advanced Research in Computer Science, 2010

Image segmentation is a critical part of clinical diagnostic tools. Medical image segmentation de... more Image segmentation is a critical part of clinical diagnostic tools. Medical image segmentation demands an efficient and robust seg-mentation algorithm against noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. The conventional fuzzy c-means algorithm is an efficient clustering algo-rithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed seg-mentation is based on improved spatial fuzzy c mean with dominant grey level of image. In this method, the color image is converted to grey level image and to make the approach more robust to noise. The input image is deniosed using an efficient denoising algorithm to decrease noise. Afterwar...

Research paper thumbnail of Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning

Biomedical Signal Processing and Control

Research paper thumbnail of Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application

Communications in computer and information science, 2022

Research paper thumbnail of Decision Making Algorithm for Blind Navigation Assistance using Deep Learning

2022 1st International Conference on Computational Science and Technology (ICCST)

Research paper thumbnail of A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation

2010 Second International conference on Computing, Communication and Networking Technologies, 2010

Research paper thumbnail of A Hybrid Region Growing Algorithm for Medical Image Segmentation

International Journal of Computer Science and Information Technology, 2012

In this paper, we have made improvements in region growing image segmentation. The First one is s... more In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliab...

Research paper thumbnail of Statistical Implementation of Segmentation of Dermoscopy Images using Multistep Region Growing

A method for segmentation of skin cancer images, firstly the algorithm automatically determines t... more A method for segmentation of skin cancer images, firstly the algorithm automatically determines the compounding colors of the lesion, and builds a number of distance images equal to the number of main colors of the lesion (reference colors). These images represent the similarity between reference colors and the other colors present in the image and they are built computing the CIEDE2000 distance in the L*a*b* color space. Texture information is also taken into account extracting the energy of some statistical moments of the L* component of the image. The method has an adaptative, N-dimensional structure where N is the number of reference colors. The segmentation is performed by a texturecontrolled multi-step region growing process. The growth tolerance parameter changes with step size and depends on the variance on each distance image for the actual grown region. Contrast is also introduced to decide the optimum value of the tolerance parameter, choosing the one which provides the r...

Research paper thumbnail of Study of medical image segmentation algorithms

Research paper thumbnail of Namib Beetle Firefly Optimization enabled Densenet architecture for hyperspectral image segmentation and classification

International Journal of Image and Data Fusion, Jan 12, 2024

Research paper thumbnail of A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. ... more Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed clustering algorithm uses fuzzy spatial information to calculate membership value. The input image is clustered using proposed ISFCM algorithm. A comparative study has been made between the conventional FCM and proposed ISFCM. The proposed approach is found to be outperforming the conventional FCM.

Research paper thumbnail of Study of medical image segmentation algorithms

Research paper thumbnail of Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning

Biomedical Signal Processing and Control, Jul 1, 2023

Research paper thumbnail of Decision Making Algorithm for Blind Navigation Assistance using Deep Learning

2022 1st International Conference on Computational Science and Technology (ICCST), Nov 9, 2022

Research paper thumbnail of A Novel Method for Character Segmentation of Vehicle License Plate

International journal of research in engineering and technology, Nov 25, 2013

Segmentation is a part of License Plate Recognition (LPR) technique, used to find out a vehicle b... more Segmentation is a part of License Plate Recognition (LPR) technique, used to find out a vehicle by its number plate without direct human involvement. Segmentation is a process of partitioning a digital image into multiple segments. The objective of segmentation is to simplify and/or change the representation of an image into something that is more expressive and easier to analyze. The proposed algorithm focuses on segmenting the characters of two rows license plate image. Before the segmentation algorithm is applied, the License plate must be localized correctly by using localization algorithm.

Research paper thumbnail of Robust segmentation algorithm using LOG edge detector for effective border detection of noisy skin lesions

... et al. developed a segmentation algorithm where the the entropy is used as stopping criterion... more ... et al. developed a segmentation algorithm where the the entropy is used as stopping criterion in the segmentation process by using recursively the mean shift filtering [8]. Teresa Mendonca et al. performed the segmentation ...

Research paper thumbnail of Optimal Routing Protocol for Wireless Sensor Network Using Genetic Fuzzy Logic System

Computers, materials & continua, 2022

The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it m... more The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data delivery is also considered to be an effective approach to save energy. To achieve this objective, we propose a new energy efficient routing protocol using genetic fuzzy logic system. Our primary objective is to save energy by sending data packets via the shortest path. Numerous studies have proved that the clustering protocol plays an important role in prolonging the life of the sensor node in the WSN. Keeping up with this our second objective is selection of head node from a cluster. This cluster head is selected based on the availability of maximum residual energy among the nodes, lifetime of head-to-head link, and its minimum distance to the base station. The genetic fitness approach is proposed for optimal routing and the selection of cluster head (CH) is employed with fuzzy logic system. As a result, the genetic fuzzy logic system (GFLS) can effectively accelerate the process to solve this problem. MATLAB is used to deploy nodes in WSN. The performance is calculated in terms of efficiency, delay, packet delivery rate and network throughput. The performance is compared with previous pertinent work. The proposed approach has elevated its performance around 8% in packet delivery and 6% in overall network throughput.

Research paper thumbnail of A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation

arXiv (Cornell University), Apr 10, 2010

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. ... more Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed clustering algorithm uses fuzzy spatial information to calculate membership value. The input image is clustered using proposed ISFCM algorithm. A comparative study has been made between the conventional FCM and proposed ISFCM. The proposed approach is found to be outperforming the conventional FCM.

Research paper thumbnail of A Robust Segmentation Approach for Noisy Medical Images Using Fuzzy Clustering With Spatial Probability

The International Arab Journal of Information Technology, 2012

Image segmentation plays a major role in medical imaging applications. During last decades, devel... more Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is highly sensitive to noise because it segments images on the basis of intensity values. In this paper, for the segmentation of noisy medical images, an effective approach is presented. The proposed approach utilizes histogram based Fuzzy C-Means clustering algorithm for the segmentation of medical images. To improve the robustness against noise, the spatial probability of the neighboring pixels is integrated in the objective function of FCM. The noisy medical images are denoised, with the help of an effective denoising algorithm, prior to segmentation, to increase further the approach's robustness. A comparative analysis is done between the conventional FCM and the proposed approach. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy despite of noise levels. From the experimental results, it is also clear that the proposed approach is more efficient and robust against noise when compared to that of the FCM.

Research paper thumbnail of Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application

Communications in computer and information science, 2022

Research paper thumbnail of A Hybrid Region Growing Algorithm for Medical Image Segmentation

International Journal of Computer Science and Information Technology, Jun 30, 2012

In this paper, we have made improvements in region growing image segmentation. The First one is s... more In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Research paper thumbnail of Hybrid Segmentation Approach using FCM and Dominant Intensity Grouping with Region Growing on Medical Image

International Journal of Advanced Research in Computer Science, 2010

Image segmentation is a critical part of clinical diagnostic tools. Medical image segmentation de... more Image segmentation is a critical part of clinical diagnostic tools. Medical image segmentation demands an efficient and robust seg-mentation algorithm against noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. The conventional fuzzy c-means algorithm is an efficient clustering algo-rithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed seg-mentation is based on improved spatial fuzzy c mean with dominant grey level of image. In this method, the color image is converted to grey level image and to make the approach more robust to noise. The input image is deniosed using an efficient denoising algorithm to decrease noise. Afterwar...

Research paper thumbnail of Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning

Biomedical Signal Processing and Control

Research paper thumbnail of Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application

Communications in computer and information science, 2022

Research paper thumbnail of Decision Making Algorithm for Blind Navigation Assistance using Deep Learning

2022 1st International Conference on Computational Science and Technology (ICCST)

Research paper thumbnail of A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation

2010 Second International conference on Computing, Communication and Networking Technologies, 2010

Research paper thumbnail of A Hybrid Region Growing Algorithm for Medical Image Segmentation

International Journal of Computer Science and Information Technology, 2012

In this paper, we have made improvements in region growing image segmentation. The First one is s... more In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliab...

Research paper thumbnail of Statistical Implementation of Segmentation of Dermoscopy Images using Multistep Region Growing

A method for segmentation of skin cancer images, firstly the algorithm automatically determines t... more A method for segmentation of skin cancer images, firstly the algorithm automatically determines the compounding colors of the lesion, and builds a number of distance images equal to the number of main colors of the lesion (reference colors). These images represent the similarity between reference colors and the other colors present in the image and they are built computing the CIEDE2000 distance in the L*a*b* color space. Texture information is also taken into account extracting the energy of some statistical moments of the L* component of the image. The method has an adaptative, N-dimensional structure where N is the number of reference colors. The segmentation is performed by a texturecontrolled multi-step region growing process. The growth tolerance parameter changes with step size and depends on the variance on each distance image for the actual grown region. Contrast is also introduced to decide the optimum value of the tolerance parameter, choosing the one which provides the r...

Research paper thumbnail of Study of medical image segmentation algorithms