Color Image Segmentation Using Semi-supervised Self-organization Feature Map

Halder, Amiya; Dalmiya, Shruti; Sadhu, Tanmana

doi:10.1007/978-3-319-04960-1_51

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The neural network reaches a high color palette variance and a better 3D RGB color space distribution of learned data from the training images than the other models. Halder et al [54] proposed to segment a color image by semi-supervised clustering method based on modal analysis and mutational agglomeration algorithm in combination with the selforganizing map. The modal analysis and mutational agglomeration algorithm is used for initial segmentation of the images.…”

Section: Neural Network Based Segmentationmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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Section: Neural Network Based Segmentationmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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“…The techniques used by Dong et al combine the working principles of SOM neural networks and simulated annealing, which support in improving the accuracy of detecting tumor regions. Halder et al suggested a semi‐supervised method using self‐organizing feature maps that start with modal analysis and mutational agglomeration to produce enhanced segmentation results. The SOM prototype mapping is performed using a 3 × 3 local window grid map that requires reduction in computational time.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.

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