Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

Patra, Dipak Kumar; Si, Tapas; Mondal, Sukumar; Mukherjee, Prakash

doi:10.1016/j.bspc.2021.102925

Cited by 32 publications

(13 citation statements)

References 50 publications

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“…Multi-Criteria Decision Analysis (MCDA) [ 2 ] has been conducted using a well-known method, namely Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and this philosophy in the selection of the best algorithm/method based on a set of performance measurement metrics is adopted from the study [ 22 , 26 – 28 ]. Here, multiple criteria are PSNR and SSIM.…”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

A novel watermarking scheme for medical image using support vector machine and lifting wavelet transform

Pal

Chowdhuri

2023

Multimed Tools Appl

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Section: Experimental Results and Comparisonsmentioning

confidence: 99%

A novel watermarking scheme for medical image using support vector machine and lifting wavelet transform

Pal

Chowdhuri

2023

Multimed Tools Appl

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“…Consider that an image is a ( k +1) homogeneous area with a k threshold gray level at t 1 , t 2 , t 3 ,…, t k , then the following equation is obtained:

where f i represents the frequency of i th gray levels, N implies the entire amount of gray levels existing from the images, and h ( i ) refers the normalization frequency. The Shannon entropy function has been defined as follows [ 21 ]:

where

…”

Section: Methodsmentioning

confidence: 99%

Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images

Vaiyapuri

Dutta²,

Sikkandar

et al. 2022

Contrast Media & Molecular Imaging

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Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization-based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon’s entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.

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“…The objective function values, the PSNR, and the computational cost were all satisfactory. Patra et al (2021) used a multilayer threshold segmentation method based on a student psychology-based optimizer for lesion detection on breast dynamic contrast-enhanced magnetic resonance imaging. The accuracy of the segmented image after feature extraction was 99.44%.…”

Section: Related Workmentioning

confidence: 99%

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

Xiao

Ye²,

Zhao³

et al. 2022

Front. Neuroinform.

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Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.

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Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

Cited by 32 publications

References 50 publications

A novel watermarking scheme for medical image using support vector machine and lifting wavelet transform

A novel watermarking scheme for medical image using support vector machine and lifting wavelet transform

Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

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