Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

Kanungo, P.; Nanda, Pradipta Kumar; Ghosh, Ashish

doi:10.5565/rev/elcvia.141

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…We visually analyzed the histogram of brain MRI with the consultation of these radiologists and image processing experts and conducted the study to find the significant and representative points for each anatomical region. The literature describes that peaks and valleys represent the object presence in the histogram [43] . Total 27 subjects (19 males and 08 females) were analyzed to derive criteria for both colorization and segmentation process (used in Eqs.…”

Section: Methodsmentioning

confidence: 99%

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

et al. 2012

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Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

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Section: Methodsmentioning

confidence: 99%

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

et al. 2012

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“…3) Compute the edge map and determine the feature entropy of the edge map of the window. 4) Choose two thresholds Th and Thf and test the conditions of the (11) and (12). 5) If the window is fixed, then start from the next window.…”

Section: Case Ii: (Wg-ii)mentioning

confidence: 99%

“…It has proven to be quite useful to separate object and background in a given scene [6]- [9], [12] or discriminate among objects having distinct gray levels. Thresholding can be categorized as bilevel and multilevel.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Thresholding Based Image Segmentation with Uneven Lighting Condition

Pradhan

Patra

Nanda³

2008

2008 IEEE Region 10 and the Third International Conference on Industrial and Information Systems

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We propose two new schemes for segmentation of images with uneven lighting conditions. These are based on adaptive window selection. The first one is a window merging method based on Lorentz information measure (LIM) but the second one is a window growing method using the notion of entropy. We propose two new window merging criterion where the window merging is carried out based on linear combination of local and global statistics. In window growing method, we define a notion of feature entropy and the window is selected employing jointly entropy and feature entropy. The two window merging schemes perform better than the schemes using only LIM. The proposed window growing technique is compared with schemes using only LIM and the proposed two merging techniques. It is found that window growing technique is best among all in the context of error due to misclassification error.

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“…In thresholding of a two class images, the pixels are divided into two groups at the threshold point. Thresholding techniques [1][2][3][4] are broadly classified into two types such as (i) global thresholding and (ii) local thesholding. In global thresholding [5] a single threshold is used to divide all the pixels into two groups.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Window Selection for Non-uniform Lighting Image Thresholding

Pattnaik

Kanungo

2021

ELCVIA

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Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu's thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches.

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Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

Cited by 10 publications

References 14 publications

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Adaptive Thresholding Based Image Segmentation with Uneven Lighting Condition

Adaptive Window Selection for Non-uniform Lighting Image Thresholding

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