A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Abstract

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

“…The elapsed time with all methods is given in Table . The proposed segmentation method (NCKMC) takes less time to find better results (1.899 seconds) than the other methods for segmentation and was better than the existing methods such as cuckoo‐based fuzzy clustering technique (CBFCT‐20.183 seconds), modified fuzzy c means (MFCM‐33.001 seconds), bias corrected fuzzy c means (BCFCM‐1492 seconds), or partial supervision fuzzy c means (PSFCM‐1671 seconds) . This is for segmentation .…”

A target region extraction method for ultrasound medical images based on improved PRIDNet and UCTransNet

“…Concerning the extraction of target regions from ultrasound images, the application of image segmentation methods facilitates the automated extraction of the designated regions of interest. Traditional machine learning methods encompass techniques such as threshold segmentation, edge detection, and region growing 9–13 . Additionally, they extend to include methods based on texture features and shape‐based for segmentation 14,15 .…”

“…Traditional machine learning methods encompass techniques such as threshold segmentation, edge detection, and region growing. [9][10][11][12][13] Additionally, they extend to include methods based on texture features and shape-based for segmentation. 14,15 Nevertheless, these approaches exhibit certain limitations in handling ultrasound images, particularly with sensitivity to their complexity, noise, and other issues related to image quality.…”

Battle royale optimizer for multi-level image thresholding

“…The initial stage in an image processing system is image segmentation, which involves grouping pixels in an image into numerous relevant homogenous sections. The problem of segmentation can be solved in a variety of ways, including region-based [1], thresholding [2], hybrid methodologies [3], boundary-based [4], and artificial neural net-works [5]. There are also hybrid segmentation techniques that employ a mix of different methodologies [6].…”