Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Alomoush, Waleed; Alrosan, Ayat; Almomani, Ammar; Alissa, Khalid; Khashan, Osama Ahmed; Al-Nawasrah, Ahmad

doi:10.11591/ijece.v11i5.pp4050-4058

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…The problem at hand is first turned into a problem of obtaining the optimum parameter vector that minimizes an objective function before using ABC. The artificial bees then select a population of starting solution vectors at random and improve them periodically using tactics such as migrating toward better solutions via a neighbor search mechanism while discarding poor solutions [6], [7].…”

Section: Proposed Program-codementioning

confidence: 99%

Iris recognition based on 2D Gabor filter

Ibrahim

Sultan²

2023

IJECE

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<span lang="EN-US">Iris recognition is a type of biometrics technology that is based on physiological features of the human body. The objective of this research is to recognize and identify iris among many irises that are stored in a visual database. This study employed a left and right iris biometric framework for inclusion decision processing by combining image processing and artificial bee colony. The proposed approach was evaluated on a visual database of 280 colored iris pictures. The database was then divided into 28 clusters. Images were preprocessed and texture features were extracted based Gabor filters to capture both local and global details within an iris. The technique begins by comparing the attributes of the online-obtained iris picture with those of the visual database. This technique either generates a reject or approve message. The consequences of the intended work reflect the output’s accuracy and integrity. This is due to the careful selection of attributes, as well as the deployment of an artificial bee colony and data clustering, which decreased complexity and eventually increased identification rate to 100%. We demonstrate that the proposed method achieves state-of-the-art performance and that our recommended procedures outperform existing iris recognition systems.</span>

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Section: Proposed Program-codementioning

confidence: 99%

Iris recognition based on 2D Gabor filter

Ibrahim

Sultan²

2023

IJECE

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“…The most well-known iterative clustering technique is FCM [22], [23]. The fuzzy c-means clustering algorithm calculates the cluster canters and the membership matrix (UM), and then minimizes an objective function J with regard to these cluster centers and membership degrees.…”

Section: Fuzzy C-means Algorithmmentioning

confidence: 99%

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Muthuswamy

Reddy²,

Narayanaswamy³

2023

IJECE

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<p>Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms.</p>

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“…A selection of key studies pertinent to this discussion is presented. Alomoush, Alrosan [30] Presented Mean ABC-SFCM, which is a reformulated version of the ABC-SFCM algorithm. Further, Alrosan, Norwawi [21] proposed a method which combines the FCM and ABC algorithms and is thus referred to as ABC-FCM, has been tested on both simulated and real brain MRI images.…”

Section: Related Workmentioning

confidence: 99%

MRI brain segmentation based on improved global best-guided artificial bee colony

Alomoush

Khashan

Alrosan

et al. 2023

Preprint

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Brain Magnetic Resonance Imaging (MRI) plays a critical role in medical research and clinical applications, ranging from quantifying tissue volume, facilitating surgical simulations, assisting in treatment planning, enabling brain mapping, aiding in disease diagnosis, and evaluating therapeutic efficacy. This study introduces a novel method for MRI brain segmentation, which harnesses the power of a hybrid approach combining Artificial Bee Colony (ABC) algorithm with Fuzzy C-Means (FCM) clustering. Our approach leverages the exploration capability of the ABC algorithm, with an improved global best guidance (IABC), to optimally initialize the cluster centroid values of the FCM, thus enhancing the segmentation outputs. Comparative evaluation of the proposed method, denoted as IABC-FCM, conducted on a diverse set of MRI brain images, reveals its superior performance. The results indicate the potential of this hybrid approach as a robust tool for improved MRI brain segmentation tasks.

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Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Cited by 12 publications

References 21 publications

Iris recognition based on 2D Gabor filter

Iris recognition based on 2D Gabor filter

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

MRI brain segmentation based on improved global best-guided artificial bee colony

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