A Comparative Analysis of Various Image Segmentation Techniques

Jaglan, Poonam; Dass, Rajeshwar; Duhan, Manoj

doi:10.1007/978-981-13-1217-5_36

Cited by 25 publications

(17 citation statements)

References 14 publications

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“…In layered-based segmentation, the image is divided into layers such as background, foreground, and mask layers. Reconstruction of the final image is decided using the mask layer [2]. This method is not widely applicable to medical image segmentation.…”

Section: Overviewmentioning

confidence: 99%

“…This method is not widely applicable to medical image segmentation. Blockbased segmentation divides the image into unequal blocks using attributes such as color, histogram, pixels, wavelet coefficients, texture, and gradient [1,2]. Block-based segmentation can be further grouped into methods based on discontinuity or similarity in the image.…”

Section: Overviewmentioning

confidence: 99%

“…Block-based segmentation can be further grouped into methods based on discontinuity or similarity in the image. It can also be further grouped into three categories: region-based, edge-or boundary-based, and hybrid techniques [1,2].…”

Section: Overviewmentioning

confidence: 99%

“…The discontinuous nature of pixels characterizes all algorithms in the edge-based segmentation family [2]. In this type of image segmentation, images are segmented into partitions based on unanticipated changes in gray intensity in the image.…”

Section: Edge-based Segmentationmentioning

confidence: 99%

“…However, pixel miscategorization errors are the main limitation of the edge-based segmentation category. The edge detection technique is an example of this class of segmentation method [2].…”

Section: Edge-based Segmentationmentioning

confidence: 99%

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Evaluation of modified adaptive k-means segmentation algorithm

et al. 2019

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Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogrambased k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Edge-based Segmentationmentioning

confidence: 99%

Section: Edge-based Segmentationmentioning

confidence: 99%

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Evaluation of modified adaptive k-means segmentation algorithm

et al. 2019

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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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Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

Kaur

GholamHosseini

Sinha

2022

Int J Imaging Syst Tech

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Automatic lesion segmentation is a key phase of skin lesion analysis that significantly increases the performance of subsequent classification steps. Segmentation is a highly complex task due to the varying nature of lesions, such as unique shapes, different colors, and structures. In this study, a two-step system is proposed comprising preprocessing algorithm and lesion segmentation network. The hairlines removal algorithm is designed using morphological operators to eliminate noise artifacts. The resulting output images are fed to the convolutional neural network (CNN) to perform lesion segmentation. The proposed CNN network is a new framework designed from scratch based on encoder-decoder architecture. The layers are stacked in a unique sequence to perform downsampling and upsampling, generating a high-resolution segmentation map. Additionally, a Tversky loss function is implemented to reduce the error rate between predicted and target output. In this study, we focus on the challenges related to the extraction of accurate lesion regions and the presence of hairlines in lesion images that can occlude important information resulting in poor segmentation. The proposed model is evaluated on four publicly available datasets, namely ISIC 2016-2018. The mean intersection over union (IoU) obtained for ISIC 2016-2018 and PH 2 is 87.1%, 77.8%, 85.2%, and 88.8%, respectively. The proposed model demonstrated higher performance as compared to other state-of-the-art methods. The hair removal step aids in the improvement of the overall performance.

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A Comparative Analysis of Various Image Segmentation Techniques

Cited by 25 publications

References 14 publications

Evaluation of modified adaptive k-means segmentation algorithm

Evaluation of modified adaptive k-means segmentation algorithm

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

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

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