Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm

Rajinikanth, V.; Raja, N. Sri Madhava; Satapathy, Suresh Chandra

doi:10.1007/978-81-322-2755-7_40

Cited by 38 publications

(16 citation statements)

References 11 publications

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“…The experimental results verify that, proposed approach is efficient in obtaining the better image quality [24][25][26][27][28] and similarity index [29][30][31] values. Finally, the proposed approach is validated using the real time brain MRI database obtained from the Bharat scans, Chennai [38].…”

Section: Introductionmentioning

confidence: 75%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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Section: Introductionmentioning

confidence: 75%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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“…In multi-level thresholding, a gray or RGB image is divided into different parts by relating similar pixels in order to trace and scrutinize significant information in the input image. The implementation of the thresholding process is essential to pre-process a raw image [33][34][35]. The Otsu and Kapur based image thresholding techniques have been extensively adopted by the researchers to threshold traditional and clinical images [36].…”

Section: Multi-level Thresholdingmentioning

confidence: 99%

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

et al. 2018

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The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.

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“…In Reference [ 18 ], the cuckoo search (CS) was applied for multi-level thresholding for gray-scale images. Also, in Reference [ 19 ], CS was applied for color images multi-level thresholding. Satapathy et al [ 20 ] presented a multi-level thresholding approach based on the chaotic bat algorithm (CBA) and Otsu as a fitness function.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Alwerfali

Al-qaness

Elaziz

et al. 2020

Entropy

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm

Cited by 38 publications

References 11 publications

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

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