Soft computing approaches for image segmentation: a survey

Chouhan, Siddharth Singh; Kaul, Ajay; Singh, Uday Pratap

doi:10.1007/s11042-018-6005-6

Cited by 95 publications

(36 citation statements)

References 247 publications

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“…In this proposed study, a multi-level threshold OSTU enhances the image with determining significant intensity point and assigns them threshold, then using combination they are enhanced by the algorithm [22].In this study, SOM performs initial clustering and this initial clustering is inclusive of FKM and average memberships are selecting, hybrid algorithm are giving accuracies for the new biomedical dataset. Soft Computing techniques are also compared with the state of art [23]. This study is based on two phases.…”

Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element.. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

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Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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“…Some unnecessary objects are obtained by the DRLSE and the LBF models in the second and third columns, and the corresponding experimental results are illustrated in Figure 3b and 3c, while the LIF model cannot deal with the second and third images in the fourth It is well known that the objective of image segmentation is to segment the whole image domain into several distinct regions in line with the regional consistency. To the best of our knowledge, most existing image segmentation techniques [2] only segment the target regions of images, which are usually not identified by tags and have no special contents. Our proposed IRLS-IS method is different from the approaches for the semantic segmentation of an image [70][71][72][73], in which the task is to predict the pixel-level category labels and recognize the objects in the image and segment them.…”

Section: Segmentation Of Single-objective Imagesmentioning

confidence: 99%

“…It is an important portion of almost all computer vision fields for real-world engineering applications, ranging from object extraction to complex medical images, satellite images, video and traffic surveillance systems, etc. [2]. As a preprocessing stage, it segments an image into different homogeneous regions according to a certain consistency.…”

Section: Introductionmentioning

confidence: 99%

“…The recently developed methods of image segmentation can be roughly divided into two categories: traditional methods and soft computing methods [2]. Since the former methods are simple and easy to implement, they have been widely applied and provide exact solutions to potential and practical applications.…”

Section: Introductionmentioning

confidence: 99%

“…Miranda et al [21] developed an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. However, the inefficiency of these approaches lies in the fact that they cannot deal with real-life complex problems that are tolerant of partial truths, imprecision, uncertainty, and approximations [2]. Note that none of these methods are good at segmenting a variety of images, and they usually perform well in one class of images while performing poorly in another class of images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Image Segmentation Method Based on Improved Regularized Level Set Model

Sun

Meng

et al. 2018

Applied Sciences

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When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

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Echo state network‐based feature extraction for efficient color image segmentation

Souahlia

Belatreche

Benyettou

et al. 2020

Concurrency and Computation

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Image segmentation plays a crucial role in many image processing and understanding applications. Despite the huge number of proposed image segmentation techniques, accurate segmentation remains a significant challenge in image analysis. This article investigates the viability of using echo state network (ESN), a biologically inspired recurrent neural network, as features extractor for efficient color image segmentation. First, an ensemble of initial pixel features is extracted from the original images and injected into the ESN reservoir. Second, the internal activations of the reservoir neurons are used as new pixel features. Third, the new features are classified using a feed forward neural network as a readout layer for the ESN. The quality of the pixel features produced by the ESN is evaluated through extensive series of experiments conducted on real world image datasets. The optimal operating range of different ESN setup parameters for producing competitive quality features is identified. The performance of the proposed ESN-based framework is also evaluated on a domain-specific application, namely, blood vessel segmentation in retinal images where experiments are conducted on the widely used digital retinal images for vessel extraction (DRIVE) dataset. The obtained results demonstrate that the proposed method outperforms state-of-the-art general segmentation techniques in terms of performance with an F-score of 0.92 ± 0.003 on the segmentation evaluation dataset. In addition, the proposed method achieves a comparable segmentation accuracy (0.9470) comparing with reported techniques of segmentation of blood vessels in images of retina and outperform them in terms of processing time. The average time required by our technique to segment one retinal image from DRIVE dataset is 8 seconds. Furthermore, empirically derived guidelines are proposed for adequately setting the ESN parameters for effective color image segmentation.

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Soft computing approaches for image segmentation: a survey

Cited by 95 publications

References 247 publications

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

An Image Segmentation Method Based on Improved Regularized Level Set Model

Echo state network‐based feature extraction for efficient color image segmentation

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