A wrapper-based approach to image segmentation and classification

Farmer, Michael; Jain, Achin

doi:10.1109/icpr.2004.1334053

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…Karnan, M. [5] suggested an algorithm which used multi-scale image segmentation. M E Jain explained a wrapper based technique for image segmentation [6]. Various techniques using fuzzy logic have also been proposed like by P.Vasuda, S.Satheesh [7] but the drawback was more computation time required, T. Logeswari, M. Karnan [8].…”

Section: Related Workmentioning

confidence: 99%

Application of Edge Detection for Brain Tumor Detection

Sharma¹,

Diwakar²,

Choudhary³

2012

IJCA

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Brain tumors are created by abnormal and uncontrolled cell division in brain itself. If the growth becomes more than 50%, then the patient is not able to recover. So the detection of brain tumor needs to be fast and accurate. The objective of this paper is to provide an efficient algorithm for detecting the edges of brain tumor. The first step starts with the acquisition of MRI scan of brain and then digital imaging techniques are applied for getting the exact location and size of tumor. MRI images consist of gray and white matter and the region containing tumor has more intensity. So, first noise filters are used for noise removal and then enhancement techniques are applied to the given MRI scan of brain. After that the basic morphological operations are applied for extracting the region suffering from tumor. And then verification of region detected is done by using watershed segmentation. General TermMorphological Segmentation

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Section: Related Workmentioning

confidence: 99%

Application of Edge Detection for Brain Tumor Detection

Sharma¹,

Diwakar²,

Choudhary³

2012

IJCA

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“…M.E. Farmer and Anil K. Jain [14] proposed in their paper a closed-loop framework for image segmentation, which uses the object classification subsystem as an integral part of the segmentation process. They proposed wrapping the segmentation and the classification together and using the classifier to determine the metric for selecting the best segmentation.…”

Section: Wrapper Approach Using Gaussian Estimatormentioning

confidence: 99%

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Agarwal¹,

Singh²

2013

IJCA

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This literature review attempts to provide a brief overview of some of the most common image segmentation techniques. It discusses Edge detection technique, Thresholding technique, Region growing based technique, Watershed technique, Compression based method, Histogram based segmentation and Graph partitioning method. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper different method of implementing genetic algorithm has been reviewed. Finally, summaries and review of research work on wrapper approach for image segmentation techniques has been represented.

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“…The spectrum of feature selection techniques is typically split into two main categories such as wrappers and filters, cf. [9,10]. Filters offer a more general view of the characterization of feature space however they cannot guarantee effectiveness as far as a specific classification scheme is concerned.…”

Section: Introductionmentioning

confidence: 99%

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Park¹,

Oh²

2012

Journal of Electrical Engineering and Technology

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-In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

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A wrapper-based approach to image segmentation and classification

Cited by 12 publications

References 15 publications

Application of Edge Detection for Brain Tumor Detection

Application of Edge Detection for Brain Tumor Detection

A Methodological Survey and Proposed Algorithm on Image Segmentation using Genetic Algorithm

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

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