Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images

Rajinikanth, Venkatesan; Dey, Nilanjan; Kavallieratou, Ergina; Lin, Hong

doi:10.1007/978-981-15-0306-1_10

Cited by 14 publications

(5 citation statements)

References 51 publications

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“…7 Results for complex blood smear images Figure 10 presents a comparison result between the earlier works performed on the LISC database, such as Chan-Vese [28], Level set [29], and Hough transform [30] with proposed VGG-UNet. The earlier works discussed in the literature [28][29][30] are hybrid image processing methods, in which the preprocessing is performed using a heuristic algorithm-assisted thresholding procedure, and mining is employed with the chosen segmentation technique. The overall accuracy by the VGG-UNet is Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In the literature, a significant amount of traditional [30,40,41] and modern (CNN) [9,11,42,43] medical segmentation techniques have been used. Implementation of the traditional segmentation techniques is time consuming, and most of the existing traditional techniques are semi-automated methods and frequently need operator assistance.…”

Section: Cnn Schemementioning

confidence: 99%

“…Rajinikanth et al[30] Hough transform-based leukocyte segmentation procedures are implemented and the outcome is evaluated against the GT. The eminence of this technique is compared and validated with other semi-automated segmentation practice available in the literature…”

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confidence: 99%

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Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

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Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

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Section: Resultsmentioning

confidence: 99%

Section: Cnn Schemementioning

confidence: 99%

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Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

Self Cite

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show abstract

“…The merit of proposed scheme is computed based on the IQM obtainable in literature. In this work, the comparison among the test image and threshold image is executed to find the essential IQM, such as Root-Mean-Squared-Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index (FSI), Structural-Similarity-Index-Measure (SSIM), Normalized-Cross-Correlation (NCC), Average-Difference (AD) and Structural-Content (SC) were computed and the information regarding this measures can be found in [20][21][22][23][24]32].…”

Section: Performance Evaluation and Validationmentioning

confidence: 99%

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

Kadry

Rajinikanth²

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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In the current era, image evaluations play a foremost role in a variety of domain, where the processing of the digital images is essential to identify the vital information. The image multi-thresholding is a vital image preprocessing field in which the available digital image is enhanced by grouping the similar pixel values. Normally, the digital test images are available in RGB/grey scale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark grey scale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO and GA.

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“…In [22], the Teaching Learning Algorithm (TLA) has been proposed to improve the circle detection problem using the gradient information in the search space. A hybrid evolutionary segmentation approach based on Kapur thresholding method and the traditional Hough transform is presented in [23]. In the paper, authors improve the search mechanism of circular shape by combining a segmentation methodology to focus the search procedure into promising search zones in the search space.…”

Section: Introductionmentioning

confidence: 99%

Multi-Circle Detection Guided by Multimodal Optimization Scheme

et al. 2023

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Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.

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Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images

Cited by 14 publications

References 51 publications

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

Multi-Circle Detection Guided by Multimodal Optimization Scheme

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